We proposed a novel bottom-up saliency detection method based on sparse representation . Saliency detection includes two elements : image representation and saliency measurement . The two elements used in our method are both biological plausible and accurate . Aiming to saliency detection problem of degraded foggy images , a new method of object saliency detection method in foggy images based on region covariance matrix is presented . In the method , color , direction and space information are extracted to form covariance feature description matrix according to characteristics of foggy images . From depth maps we extract geometry related features that are further fused with color contrast . We solve saliency detection problem in segment-wise domain that allows prediction of objects rather than separate pixels . Modelling of saliency is done using conditional random field that allows for pairwise dependencies of segments . Saliency object detection has been a very active research topic recently , due to its extensive applications in image compression , scene understanding , image retrieval , and so forth . The overwhelming majority of existing computational models are designed based on computer vision techniques by using a lot of image cues and priors . Most image retargeting algorithms rely heavily on valid saliency map detection to proceed . But the inefficiency of high quality saliency map detection severely restricts applications of these image retargeting methods . In this paper , we describe a stochastic algorithm for efficient context-aware saliency map detection . Many researches have attempted to propose effective model to detect the saliency area which is corresponding to human perception . Therefore , this research focuses on improving the saliency detection process by proposing the improved adaptive spectrum scale-space ( IASSS ) . The main contributions of the proposed method include ( i ) scale-and-space Gaussian filter ( AS2 G filter ) and ( ii ) the new method for saliency map selection based on local entropy . Then , a CRF is designed to robustly detect salient regions utilising neighbourhood consistency . By inferring the CRF via belief propagation algorithm , we actually make use of the global statistic information in the saliency detection process . Experimental results demonstrate the robustness and the effectiveness of the proposed method . Purpose The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization ( CPIO ) , which can effectively improve the computing efficiency of the basic Itti ’s model for saliency-based detection . The CPIO algorithm and relevant applications are aimed at air surveillance for target detection . Another problem is that they have used redundant information to generate an acceptable saliency map such as variances in different color spaces , multi-scale features and so on . In this paper , we propose saliency detecting with a diffusion model ; use optimal seeds generated from foreground statistic cue , i.e. , the compactness . Each superpixel is considered as a node and a fully connected graph is constructed to calculate the global compactness of each node . The current research in this direction , however , is limited by the lack of a comprehensive benchmark . This work contributes such a RGB-T image dataset , which includes 821 spatially aligned RGB-T image pairs and their ground truth annotations for saliency detection purpose . The image pairs are with high diversity recorded under different scenes and environmental conditions , and we annotate 11 challenges on these image pairs for performing the challenge-sensitive analysis for different saliency detection algorithms . A saliency detection method is applied to the enhanced images to highlight the initial polyp candidates . In the classification stage , polyp candidates are selected after performing an image enhancement step and a saliency detection step . Exhaustive experiments have been performed on three publicly available databases : CVC ColonDB , CVC ClinicDB , and ETIS Larib to evaluate the performance of the proposed polyp detection algorithm . Intuitively , by simulating the selective attention mechanism of human visual system , saliency detection technique can select attractive objects in scenes and thus is a potential way to provide useful priors for WOD . However , the way to adopt saliency detection in WOD is not trivial since the detected saliency region might be possibly highly ambiguous in complex cases . To this end , this paper first comprehensively analyzes the challenges in applying saliency detection to WOD . In such chicken - egg problem , most methods ( including the proposed one ) usually use the alternative learning strategy that first provides some coarse estimation to initialize the potential object locations and then gradually train the object detectors and update object locations jointly . In this paper , we leverage saliency detection to initialize the potential object locations due to the following reasons : 1 ) Saliency detection ; [ reference ][ reference ] aims at simulating the selective attention mechanism of human visual system to automatically select sub - regions ( usually the regions containing objects of interest ) in image scenes . Thus , it can be readily utilized to provide useful priors to estimate the potential object localizations and fit well to the investigated task . Thus , as shown in Fig . 1 , in the images only containing one category of objects ( considered as " easy " images ) , the objects can be captured by saliency detection methods easily and associated with the corresponding image label properly . Whereas in the images weakly labelled as containing multiple categories of objects ( considered as " hard " images ) , objects in all of categories will have the probabilities to attract the human attention and their corresponding locations are also hard for saliency models to identify , which largely increases the ambiguity when considering to apply the obtained salient detection results to initializing the training samples for WOD . Thus , it is unreliable to directly apply saliency detection to WOD . Specifically , we first obtain the " easy " images based on the number of weak labels of each image , i.e. , images weakly labelled as only containing one object category are considered as " easy " . Then , for each " easy " image , we adopt an unsupervised saliency detection method , i.e. , RC [ reference ] in this paper due to its efficiency , to generate the corresponding saliency estimation . Finally , the important weights v are initialized as the intersection - over - union ( IOU ) score between each hypothesis and the salient region . We discover an emotion prioritization effect : for our images , emotion-eliciting content attracts human attention strongly , but such advantage diminishes dramatically after initial fixation . Aiming to model the human emotion prioritization computationally , we design a deep neural network for saliency prediction , which includes a novel subnetwork that learns the spatial and semantic context of the image scene . The proposed network outperforms the state-of-the-art on three benchmark datasets , by effectively capturing the relative importance of human attention within an image . One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures . In this work , we propose an end-to-end dilated inception network ( DINet ) for visual saliency prediction . It captures multi-scale contextual features effectively with very limited extra parameters . Previous works suggest that scene contours play important roles in guiding visual attention . In this study , a computational model is proposed to improve the performance in visual saliency prediction by integrating the low- and mid-level visual cues and evaluate the contribution of scene contours in guiding visual attention . Firstly , we define three kinds of Gestalt principles based on mid-level cues , including contour density , closure , and symmetry to characterize the potential salient regions . Since DenseNet has shown the excellent results on image classification tasks , the coarse feature maps from the fully convolutional neural networks based on DenseNets were concatenated to predict saliency maps through a readout network . It is shown that the DenseNet is useful for the saliency-map prediction and achieved the state-of-the-art accuracy on the major fixation datasets . Visual saliency detection tries to mimic human vision psychology which concentrates on sparse , important areas in natural image . Saliency prediction research has been traditionally based on low level features such as contrast , edge , etc . Recent thrust in saliency prediction research is to learn high level semantics using ground truth eye fixation datasets . In this paper we present , WEPSAM : Weakly Pre-Learnt Saliency Model as a pioneering effort of using domain specific pre-learing on ImageNet for saliency prediction using a light weight CNN architecture . The second one is implementing the end-to-end architecture for the saliency detection . Experiment results on CAT2000 , SALICON , MIT300 and iSUN demonstrate that the proposed method achieves state-of-the-art results in saliency detection and prediction tasks . Furthermore , the analysis of different saliency evaluation metrics related to the results of experiment is provided . The proposed framework is inspired by psychological and neurological studies that emotion attracts attention . Using three publicly available datasets with emotion-rich images , we were able to show that awareness of the emotion-inducing region improves saliency prediction of images . Saliency metrics for probabilistic models , particularly information gain and KL-divergence , have improved with respect to the same architecture without emotion information . Furthermore , it is unclear that intermediate levels of saliency ( ie , neither very high , nor very low ) are meaningful : Something is either salient , or it is not . Drawing from those two observations , we reformulate the saliency prediction problem as a salient region \textit{segmentation } problem . We demonstrate that the reformulation allows for faster convergence than the classical regression problem , while performance is comparable to state-of-the-art . In the step of local pooling , we investigate the effect of multi-scale contextual information for saliency detection and find that there exists an optimal contextual scale to achieve the patch-level feature presentation . On basis of this observation , we propose an approach for automatic scale selection in saliency prediction step . The experimental results demonstrate that our method can effectively improve the performance of goal-driven saliency detection as well as related object detection . The existing multi-task CNN-based NR-IQA methods which usually consider distortion identification as the auxiliary sub-task can not accurately identify the complex mixtures of distortions exist in authentically distorted images . By contrast , our saliency prediction sub-task is more universal because visual attention always exists when viewing every image , regardless of its distortion type . More importantly , related works have reported that saliency information is highly correlated with image quality while this property is fully utilized in our proposed SGNet by training the model with more informative labels including saliency maps and quality scores simultaneously . For various image processing applications , saliency detection can be used to extract the regions of interest for images . Compared with images , the motion feature has to be considered for video saliency detection . This paper proposes a new video saliency detection model based on human visual acuity and spatiotemporal cues in cloud systems . The salient segments are then extracted by applying a spectral foreground detection method , quantum cuts , on this graph . We evaluate our approach on several public datasets for video saliency and activity localization to demonstrate the favorable performance of the proposed video quantum cuts compared to the state of the art . This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos . First , we construct the tensor representation of video frames . Enormous research efforts have been focused on saliency detection , but it still has some issues in maintaining spatiotemporal consistency of videos and uniformly highlighting entire objects . To address these issues , this paper proposes a superpixel-level spatiotemporal saliency model for saliency detection in videos . To detect salient object , we extract multiple spatiotemporal features combined with intra-consistency motion information preliminarily . Therefore , video saliency estimation methods should differ substantially from image saliency methods . In this paper we propose a novel method for video saliency estimation , which is inspired by the way people watch videos . We explicitly model the continuity of the video by predicting the saliency map of a given frame , conditioned on the map from the previous frame . Secondly , we combine motion feature with the spatial feature to represent dynamic saliency in temporal domain . We test our method in both image based saliency extraction and video saliency extraction . The results illustrate its high performance in both two tasks . Different from previous work , our research focuses on two aspects : 1 ) dynamic synopsis which replaces fixed video summary ; 2 ) dynamic recommendation scheme which replaces retrieval technique . Promising results demonstrate that our approach can discover the latent relationship among video shots with video saliency extraction , semantic annotation and user preference analysis . Therefore , the proposed framework can effectively lead viewers to preview novel movies in individual manner . Extensive efforts have been made in the past decades . Nevertheless , video-based saliency detection and foreground segmentation remained challenging . On the one hand , the performances of image-based saliency detection algorithms are limited in complex contents , while the temporal connectivity between frames are not well-resolved . On the other hand , compared with the prosperous image-based datasets , the datasets in video-level saliency detection and segmentation usually have smaller scale and less diversity of contents . Towards a better understanding of video-level semantics , this thesis investigates the foreground estimation and segmentation in both image-level and video-level . To evaluate the performance of our approach , we present a new comprehensive database of 2D viewing eye-fixation ground-truth for RGBD videos . Our experiments indicate that it is beneficial to integrate depth into video saliency estimates for content that is viewed on a 2D display . We demonstrate that our approach outperforms state-of-the-art methods for video saliency , achieving 15 % relative improvement . In this paper , we present a probabilistic multi-task learning approach for visual saliency estimation in video . In our approach , the problem of visual saliency estimation is modeled by simultaneously considering the stimulus-driven and task-related factors in a probabilistic framework . As an emerging vision platform , a drone can look from many abnormal viewpoints which brings many new challenges into the classic vision task of video saliency prediction . To investigate these challenges , this paper proposes a large-scale video dataset for aerial saliency prediction , which consists of ground-truth salient object regions of 1,000 aerial videos , annotated by 24 subjects . To the best of our knowledge , it is the first large-scale video dataset that focuses on visual saliency prediction on drones . Based on this dataset , we propose a Model-guided Multi-path Network ( MM-Net ) that serves as a baseline model for aerial video saliency prediction . Inspired by the annotation process in eye-tracking experiments , MM-Net adopts multiple information paths , each of which is initialized under the guidance of a classic saliency model .
We proposed a novel bottom-up saliency detection method based on sparse representation . Saliency detection includes two elements : image representation and saliency measurement . The two elements used in our method are both biological plausible and accurate .
Aiming to saliency detection problem of degraded foggy images , a new method of object saliency detection method in foggy images based on region covariance matrix is presented . In the method , color , direction and space information are extracted to form covariance feature description matrix according to characteristics of foggy images .
From depth maps we extract geometry related features that are further fused with color contrast . We solve saliency detection problem in segment-wise domain that allows prediction of objects rather than separate pixels . Modelling of saliency is done using conditional random field that allows for pairwise dependencies of segments .
Saliency object detection has been a very active research topic recently , due to its extensive applications in image compression , scene understanding , image retrieval , and so forth . The overwhelming majority of existing computational models are designed based on computer vision techniques by using a lot of image cues and priors .
Most image retargeting algorithms rely heavily on valid saliency map detection to proceed . But the inefficiency of high quality saliency map detection severely restricts applications of these image retargeting methods . In this paper , we describe a stochastic algorithm for efficient context-aware saliency map detection .
Many researches have attempted to propose effective model to detect the saliency area which is corresponding to human perception . Therefore , this research focuses on improving the saliency detection process by proposing the improved adaptive spectrum scale-space ( IASSS ) . The main contributions of the proposed method include ( i ) scale-and-space Gaussian filter ( AS2 G filter ) and ( ii ) the new method for saliency map selection based on local entropy .
Then , a CRF is designed to robustly detect salient regions utilising neighbourhood consistency . By inferring the CRF via belief propagation algorithm , we actually make use of the global statistic information in the saliency detection process . Experimental results demonstrate the robustness and the effectiveness of the proposed method .
Purpose The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization ( CPIO ) , which can effectively improve the computing efficiency of the basic Itti ’s model for saliency-based detection . The CPIO algorithm and relevant applications are aimed at air surveillance for target detection .
Another problem is that they have used redundant information to generate an acceptable saliency map such as variances in different color spaces , multi-scale features and so on . In this paper , we propose saliency detecting with a diffusion model ; use optimal seeds generated from foreground statistic cue , i.e. , the compactness . Each superpixel is considered as a node and a fully connected graph is constructed to calculate the global compactness of each node .
The current research in this direction , however , is limited by the lack of a comprehensive benchmark . This work contributes such a RGB-T image dataset , which includes 821 spatially aligned RGB-T image pairs and their ground truth annotations for saliency detection purpose . The image pairs are with high diversity recorded under different scenes and environmental conditions , and we annotate 11 challenges on these image pairs for performing the challenge-sensitive analysis for different saliency detection algorithms .
A saliency detection method is applied to the enhanced images to highlight the initial polyp candidates . In the classification stage , polyp candidates are selected after performing an image enhancement step and a saliency detection step . Exhaustive experiments have been performed on three publicly available databases : CVC ColonDB , CVC ClinicDB , and ETIS Larib to evaluate the performance of the proposed polyp detection algorithm .
Intuitively , by simulating the selective attention mechanism of human visual system , saliency detection technique can select attractive objects in scenes and thus is a potential way to provide useful priors for WOD . However , the way to adopt saliency detection in WOD is not trivial since the detected saliency region might be possibly highly ambiguous in complex cases . To this end , this paper first comprehensively analyzes the challenges in applying saliency detection to WOD .
In such chicken - egg problem , most methods ( including the proposed one ) usually use the alternative learning strategy that first provides some coarse estimation to initialize the potential object locations and then gradually train the object detectors and update object locations jointly . In this paper , we leverage saliency detection to initialize the potential object locations due to the following reasons : 1 ) Saliency detection ; [ reference ][ reference ] aims at simulating the selective attention mechanism of human visual system to automatically select sub - regions ( usually the regions containing objects of interest ) in image scenes . Thus , it can be readily utilized to provide useful priors to estimate the potential object localizations and fit well to the investigated task .
Thus , as shown in Fig . 1 , in the images only containing one category of objects ( considered as " easy " images ) , the objects can be captured by saliency detection methods easily and associated with the corresponding image label properly . Whereas in the images weakly labelled as containing multiple categories of objects ( considered as " hard " images ) , objects in all of categories will have the probabilities to attract the human attention and their corresponding locations are also hard for saliency models to identify , which largely increases the ambiguity when considering to apply the obtained salient detection results to initializing the training samples for WOD . Thus , it is unreliable to directly apply saliency detection to WOD .
Specifically , we first obtain the " easy " images based on the number of weak labels of each image , i.e. , images weakly labelled as only containing one object category are considered as " easy " . Then , for each " easy " image , we adopt an unsupervised saliency detection method , i.e. , RC [ reference ] in this paper due to its efficiency , to generate the corresponding saliency estimation . Finally , the important weights v are initialized as the intersection - over - union ( IOU ) score between each hypothesis and the salient region .
We discover an emotion prioritization effect : for our images , emotion-eliciting content attracts human attention strongly , but such advantage diminishes dramatically after initial fixation . Aiming to model the human emotion prioritization computationally , we design a deep neural network for saliency prediction , which includes a novel subnetwork that learns the spatial and semantic context of the image scene . The proposed network outperforms the state-of-the-art on three benchmark datasets , by effectively capturing the relative importance of human attention within an image .
One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures . In this work , we propose an end-to-end dilated inception network ( DINet ) for visual saliency prediction . It captures multi-scale contextual features effectively with very limited extra parameters .
Previous works suggest that scene contours play important roles in guiding visual attention . In this study , a computational model is proposed to improve the performance in visual saliency prediction by integrating the low- and mid-level visual cues and evaluate the contribution of scene contours in guiding visual attention . Firstly , we define three kinds of Gestalt principles based on mid-level cues , including contour density , closure , and symmetry to characterize the potential salient regions .
Since DenseNet has shown the excellent results on image classification tasks , the coarse feature maps from the fully convolutional neural networks based on DenseNets were concatenated to predict saliency maps through a readout network . It is shown that the DenseNet is useful for the saliency-map prediction and achieved the state-of-the-art accuracy on the major fixation datasets .
Visual saliency detection tries to mimic human vision psychology which concentrates on sparse , important areas in natural image . Saliency prediction research has been traditionally based on low level features such as contrast , edge , etc . Recent thrust in saliency prediction research is to learn high level semantics using ground truth eye fixation datasets . In this paper we present , WEPSAM : Weakly Pre-Learnt Saliency Model as a pioneering effort of using domain specific pre-learing on ImageNet for saliency prediction using a light weight CNN architecture .
The second one is implementing the end-to-end architecture for the saliency detection . Experiment results on CAT2000 , SALICON , MIT300 and iSUN demonstrate that the proposed method achieves state-of-the-art results in saliency detection and prediction tasks . Furthermore , the analysis of different saliency evaluation metrics related to the results of experiment is provided .
The proposed framework is inspired by psychological and neurological studies that emotion attracts attention . Using three publicly available datasets with emotion-rich images , we were able to show that awareness of the emotion-inducing region improves saliency prediction of images . Saliency metrics for probabilistic models , particularly information gain and KL-divergence , have improved with respect to the same architecture without emotion information .
Furthermore , it is unclear that intermediate levels of saliency ( ie , neither very high , nor very low ) are meaningful : Something is either salient , or it is not . Drawing from those two observations , we reformulate the saliency prediction problem as a salient region \textit{segmentation } problem . We demonstrate that the reformulation allows for faster convergence than the classical regression problem , while performance is comparable to state-of-the-art .
In the step of local pooling , we investigate the effect of multi-scale contextual information for saliency detection and find that there exists an optimal contextual scale to achieve the patch-level feature presentation . On basis of this observation , we propose an approach for automatic scale selection in saliency prediction step . The experimental results demonstrate that our method can effectively improve the performance of goal-driven saliency detection as well as related object detection .
The existing multi-task CNN-based NR-IQA methods which usually consider distortion identification as the auxiliary sub-task can not accurately identify the complex mixtures of distortions exist in authentically distorted images . By contrast , our saliency prediction sub-task is more universal because visual attention always exists when viewing every image , regardless of its distortion type . More importantly , related works have reported that saliency information is highly correlated with image quality while this property is fully utilized in our proposed SGNet by training the model with more informative labels including saliency maps and quality scores simultaneously .
For various image processing applications , saliency detection can be used to extract the regions of interest for images . Compared with images , the motion feature has to be considered for video saliency detection . This paper proposes a new video saliency detection model based on human visual acuity and spatiotemporal cues in cloud systems .
The salient segments are then extracted by applying a spectral foreground detection method , quantum cuts , on this graph . We evaluate our approach on several public datasets for video saliency and activity localization to demonstrate the favorable performance of the proposed video quantum cuts compared to the state of the art .
This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos . First , we construct the tensor representation of video frames .
Enormous research efforts have been focused on saliency detection , but it still has some issues in maintaining spatiotemporal consistency of videos and uniformly highlighting entire objects . To address these issues , this paper proposes a superpixel-level spatiotemporal saliency model for saliency detection in videos . To detect salient object , we extract multiple spatiotemporal features combined with intra-consistency motion information preliminarily .
Therefore , video saliency estimation methods should differ substantially from image saliency methods . In this paper we propose a novel method for video saliency estimation , which is inspired by the way people watch videos . We explicitly model the continuity of the video by predicting the saliency map of a given frame , conditioned on the map from the previous frame .
Secondly , we combine motion feature with the spatial feature to represent dynamic saliency in temporal domain . We test our method in both image based saliency extraction and video saliency extraction . The results illustrate its high performance in both two tasks .
Different from previous work , our research focuses on two aspects : 1 ) dynamic synopsis which replaces fixed video summary ; 2 ) dynamic recommendation scheme which replaces retrieval technique . Promising results demonstrate that our approach can discover the latent relationship among video shots with video saliency extraction , semantic annotation and user preference analysis . Therefore , the proposed framework can effectively lead viewers to preview novel movies in individual manner .
Extensive efforts have been made in the past decades . Nevertheless , video-based saliency detection and foreground segmentation remained challenging . On the one hand , the performances of image-based saliency detection algorithms are limited in complex contents , while the temporal connectivity between frames are not well-resolved . On the other hand , compared with the prosperous image-based datasets , the datasets in video-level saliency detection and segmentation usually have smaller scale and less diversity of contents . Towards a better understanding of video-level semantics , this thesis investigates the foreground estimation and segmentation in both image-level and video-level .
To evaluate the performance of our approach , we present a new comprehensive database of 2D viewing eye-fixation ground-truth for RGBD videos . Our experiments indicate that it is beneficial to integrate depth into video saliency estimates for content that is viewed on a 2D display . We demonstrate that our approach outperforms state-of-the-art methods for video saliency , achieving 15 % relative improvement .
In this paper , we present a probabilistic multi-task learning approach for visual saliency estimation in video . In our approach , the problem of visual saliency estimation is modeled by simultaneously considering the stimulus-driven and task-related factors in a probabilistic framework .
As an emerging vision platform , a drone can look from many abnormal viewpoints which brings many new challenges into the classic vision task of video saliency prediction . To investigate these challenges , this paper proposes a large-scale video dataset for aerial saliency prediction , which consists of ground-truth salient object regions of 1,000 aerial videos , annotated by 24 subjects . To the best of our knowledge , it is the first large-scale video dataset that focuses on visual saliency prediction on drones . Based on this dataset , we propose a Model-guided Multi-path Network ( MM-Net ) that serves as a baseline model for aerial video saliency prediction . Inspired by the annotation process in eye-tracking experiments , MM-Net adopts multiple information paths , each of which is initialized under the guidance of a classic saliency model .
Saliency Detection Based on Global and Local Short-term Sparse Representation A New Method of Object Saliency Detection in Foggy Images Salient object detection using scene layout estimation Object-Based Visual Saliency via Laplacian Regularized Kernel Regression Stochastic Context-Aware Saliency Detection Improved Adaptive Spectrum Scale-Space in Frequency Domain for Saliency Detection Conditional random field-based mesh saliency Comparative study of swarm intelligence-based saliency computation Saliency Detection by Compactness Diffusion A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel Approach Computer-aided polyp detection based on image enhancement and saliency-based selection Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-Paced Curriculum Learning Emotional Attention: A Study of Image Sentiment and Visual Attention A Dilated Inception Network for Visual Saliency Prediction Fixation Prediction based on Scene Contours Fully Convolutional DenseNet for Saliency-Map Prediction WEPSAM: Weakly Pre-Learnt Saliency Model Saliency Detection in Images with Complex Background by End-to-End Sparse Maxout CNN Modeling human attention by learning from large amount of emotional images Salient Region Segmentation Top-Down Saliency by Multi-scale Contextual Pooling SGDNet: An End-to-End Saliency-Guided Deep Neural Network for No-Reference Image Quality Assessment An Effective Video Saliency Detection Model Based on Human Visual Acuity and Spatiotemporal Cues in Cloud Systems Spatiotemporal Saliency Estimation by Spectral Foreground Detection Tensor-based spatiotemporal saliency detection Spatiotemporal saliency based on location prior model Learning Video Saliency from Human Gaze Using Candidate Selection A video saliency detection method based on spatial and motion information An interactive method for dynamic video synopsis generation An investigation into image and video foreground segmentation and change detection Learning Gaze Transitions from Depth to Improve Video Saliency Estimation Probabilistic Multi-Task Learning for Visual Saliency Estimation in Video Model-Guided Multi-Path Knowledge Aggregation for Aerial Saliency Prediction
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ICIG (1) MMSP ICIP BMVC IJCAI CVPR SSCI ACPR BigData PCM ACM Multimedia IJCNN CVPR ICIP
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