Self-explanatory Sparse Representation for Image Classification

Abstract

Abstract. Traditional sparse representation algorithms usually operate in a single Euclidean space. This paper leverages a self-explanatory reformulation of sparse representation, i.e., linking the learned dictionary atoms with the original feature spaces explicitly, to extend simultaneous dictionary learning and sparse coding into reproducing kernel Hilbert spaces (RKHS). The resulting single-view self-explanatory sparse representation (SSSR) is applicable to an arbitrary kernel space and has the nice property that the derivatives with respect to parameters of the coding are independent of the chosen kernel. With SSSR, multiple-view self-explanatory sparse representation (MSSR) is proposed to capture and combine various salient regions and structures from different kernel spaces. This is equivalent to learning a nonlinear structured dictionary, whose complexity is reduced by learning a set of smaller dictionary blocks via SSSR. SSSR and MSSR are then incorporated into a spatial pyramid matching framework and developed for image classification. Extensive experimental results on four benchmark datasets, including UIUC-Sports, Scene 15, Caltech-101, and Caltech-256, demonstrate the effectiveness of our proposed algorithm.

Laplace Graph Embedding Class Specific Dictionary Learning for Face Recognition

“…Liu et al [29] proposed a single-view self-explanatory sparse representation dictionary learning algorithm (SSSR). Supposing that represents the class number of the training samples and means the collection of sample characteristics of class , the objective function of the SSSR method can be formulated as…”

“…Meanwhile, Liu et al [29] extended the SSSR algorithm into kernel spaces, which can map the original sample features into a high dimensional nonlinear space for better mining of nonlinear relationships between samples. The objective function of the multiple-view kernel-based class specific dictionary learning algorithm (KCSDL) is shown as follows:…”

“…According to the solving method in [29], it is easy to obtain the solution of the minimum value of ( ) under the current iteration condition:…”

“…The proposed LGECSDL algorithm is compared with another seven classical face recognition algorithms: nearest neighbor (NN) classification, collaborative representation based classification (CRC) [30], sparse representation based classification (SRC) [31], kernel-based probabilistic collaborative representation based classifier (ProKCRC) [32], VGG19 [33], kernel-based class specific dictionary learning (KCSDL) algorithm [29], and SVM [34].…”

“…Nguyen et al [28] presented a dictionary learning method for sparse representation based on the kernel method. Liu et al [29] proposed a multiple-view self-explanatory sparse representation dictionary learning algorithm (MSSR) to capture and combine various salient regions and structures from different kernel spaces, and this method achieved superior performance in the field of face recognition.…”