The quality of the features used in visual recognition is of fundamental importance for the overall system. For a long time, low-level hand-designed feature algorithms as SIFT and HOG have obtained the best results on image recognition. Visual features have recently been extracted from trained convolutional neural networks. Despite the high-quality results, one of the main drawbacks of this approach, when compared with hand-designed features, is the training time required during the learning process. In this paper, we propose a simple and fast way to train supervised convolutional models to feature extraction while still maintaining its high-quality. This methodology is evaluated on different datasets and compared with state-of-the-art approaches.
A simple fast method for extracting visual features from convolutional neural networks Proposes a fast way to learn convolutional features that later can be used with any classifier by using reduced numbers of training epocs and specific schedule delays of learning rate Use a learning rate decay scheme that is fixed relative to the number of epochs used in training and extract the penultimate layer output as features to train a conventional classifier.