Ambiguity or uncertainty is a pervasive element of many real-world decision-making processes. Variation in decisions is a norm in this situation when the same problem is posed to different subjects. Psychological and metaphysical research has proven that decision making by humans is subjective. It is influenced by many factors such as experience, age, background, etc. Scene understanding is one of the computer vision problems that fall into this category. Conventional methods relax this problem by assuming that scene images are mutually exclusive; therefore, they focus on developing different approaches to perform the binary classification tasks. In this paper, we show that scene images are nonmutually exclusive and propose the fuzzy qualitative rank classifier (FQRC) to tackle the aforementioned problems. The proposed FQRC provides a ranking interpretation instead of binary decision. Evaluations in terms of qualitative and quantitative measurements using large numbers and challenging public scene datasets have shown the effectiveness of our proposed method in modeling the nonmutually exclusive scene images.
However, in scene classification task, it is very likely that a scene image can belongs to multiple classes. As a result of this, all the aforementioned solutions that assumed scene classes are mutually exclusive are not practical and often lead to classification errors. We believe that scene images are somewhat arbitrary and possibly sub-optimal as depicted in Fig. . To the best of our knowledge, there are numerous multi-label classification research @cite_29 @cite_25 ; however, only a few were focused in the domain of scene understanding. @cite_7 proposed an approach using SVM with cross-training to build the classifier for every base class. Then maximum a posteriori (MAP) principle is applied with the aid of prior probability calculation and gamma fit operation toward the single and multi-label training data. This is to obtain the desired threshold to determine whether a testing sample is fall into single label event or multiple label events.
@cite_29 @cite_25 @cite_7
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Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set. In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive. Such problems arise in semantic scene and document classification and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a field scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a different treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classification; furthermore, our work appears to generalize to other classification problems of the same nature.