Cross-Label Suppression: A Discriminative and Fast Dictionary Learning With Group Regularization

Abstract

This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose cross-label suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we don't resort to frequently-used 0 -norm or 1 -norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets including face recognition, object categorization, scene classification, texture recognition and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.

Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification

“…In this section, we compare LPLC-HDL with the representative dictionary learning methods including D-KSVD [5], LC-KSVD [6], LCLE-DL [7], FDDL [13], DL-COPAR [16] and CLSDDL [19] on the Yale face dataset [29], the Extended YaleB face dataset [30], the Labeled Faces in the Wild (LFW) dataset [31] for face recognition, the Caltech-101 object dataset [23] and the Oxford 102 Flowers dataset [32] for object classification and flower classification, respectively. Moreover, we further compare it with the Sparse Representation based Classification (SRC) [33], the Collaborative Representation based Classifier (CRC) [34], the Probabilistic CRC (ProCRC) [35], the Sparsity Augmented Collaborative Representation (SACR) [36] and some other state-of-the-art methods on the particular datasets.…”

“…In recent years, the hybrid DL [16,19,25,26] has been getting more and more attention in the classification problem. The hybrid dictionary has been shown to perform better than the other types of dictionaries, as it can preserve both the class-specific and common information of the data.…”

“…To obtain the particularity representation for the classification, we will assign the labels to the atoms of the class-specific dictionary, as presented in [7,19]. If an atom…”

“…Gao et al [18] developed a Category-specific and Shared Dictionary Learning (CSDL) approach for fine-grained image classification. Wang et al [19] designed a structured dictionary consisting of label-particular atoms corresponding to some class and shared atoms commonly used by all the classes, and introduced a Cross-Label Suppression for Dictionary Learning (CLSDL) to generate approximate sparse coding vectors for classification. To some extent, the hybrid dictionary is very effective at preserving the complex structure of the visual data.…”

Locality Constraint Dictionary Learning With Support Vector for Pattern Classification

“…Sun et al [27] presented a discriminative group sparse dictionary learning (DGSDL) model which learns a class-specific subdictionary for each class as well as a common sub-dictionary shared by all classes. By introducing a cross-label suppression constraint and group regularization term into the framework of SDL, Wang et al [28] designed a cross-label suppression discriminative DL (CLS-DDL) approach. Lin et al [29] proposed a robust, discriminative and comprehensive dictionary learning (RDCDL) model which learns a class-shared dictionary, class-specific dictionaries and a disturbance dictionary to represent the commonality, particularity and disturbance components in the data.…”