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Image representation based on sparse coding generalizes the bag of words model. Although it reduces the reconstruction error for local features to achieve the state-of-the-art image classification performance, the large computational cost hinders the application of sparse coding-based image features. In this paper, we propose approximating a sparse code using the output of a simple neural network. The resulting parameter learning model for the neural network automatically incorporates non-negative and shift-invariant constraints, leading to an efficient normalized non-negative sparse coding (N3SC) sparse encoder. Without the use of the traditional iterative process to solve the sparse coding objective, the sparse encoder directly “converts” each local feature into a sparse code. We also introduce a method for training the encoder based on the auto-encoder method. In addition, we formally propose the corresponding sparse coding scheme called N3SC, which enforces both the non-negative constraint and the shift-invariant constraint in addition to the traditional sparse coding criteria. As demonstrated by several experiments, the obtained N3SC encoder requires only 3%–10% of the processing time for image feature extraction compared with the standard sparse coding scheme. At the same time, the features extracted using the exact solutions of the N3SC coding scheme and the N3SC encoder offer superior image classification accuracy compared to the accuracy of many existing sparse coding-based representations. [ABSTRACT FROM AUTHOR]

To study underlying possibilities for the successes of conventional image representation and deep neural networks (DNNs) in image representation, we propose a deep feature extraction, encoding, and pooling network (DEFEATnet) architecture, which is a marriage between conventional image representation approaches and DNNs. In particular, in DEFEATnet, each layer consists of three components: feature extraction, feature encoding, and pooling. The primary advantage of DEFEATnet is twofold. First, it consolidates the prior knowledge (e.g., translation invariance) from extracting, encoding, and pooling handcrafted features, as in the conventional feature representation approaches. Second, it represents the object parts at different granularities by gradually increasing the local receptive fields in different layers, as in DNNs. Moreover, DEFEATnet is a generalized framework that can readily incorporate all types of local features as well as all kinds of well-designed feature encoding and pooling methods. Since prior knowledge is preserved in DEFEATnet, it is especially useful for image representation on small/medium size data sets, where DNNs usually fail due to the lack of sufficient training data. Promising experimental results clearly show that DEFEATnets outperform shallow conventional image representation approaches by a large margin when the same type of features, feature encoding and pooling are used. The extensive experiments also demonstrate the effectiveness of the deep architecture of our DEFEATnet in improving the robustness for image presentation. [ABSTRACT FROM AUTHOR]