Low-rank Sparse Coding Research Articles

Low-rank sparse coding and region of interest pooling for dynamic 3D facial expression recognition

In this paper, we propose a dynamic three-dimensional facial expression recognition using low-rank sparse codes pooled from automatically detected regions of interests. Low-rank sparse coding has been applied for the first time in dynamic facial expression recognition and a novel region-based pooling is suggested. Twelve regions of interests are defined using detected landmarks based on facial expression activation dynamics. Landmarks are tracked in subsequent frames using multi-point tracker. Temporal segmentation is utilized by using geometric information extracted from detected landmarks. LBP-TOP feature descriptors are extracted from cuboids inside spatiotemporal regions of interests in both texture and depth sequences. Texture and depth features are then fused to form the feature matrix. Low-rank sparse coding is utilized to obtain sparse codes from feature descriptors. Finally, hidden-state conditional random fields are employed to classify six basic expressions. Experimental results and comparison with recent studies verify that proposed method improves the accuracy of dynamic facial expression recognition. The average accuracy of recognition of six basic expressions is improved by the proposed system from 83.07 to 85.12\(\%\) on 95 subjects in BU-4DFE data set.

A Denoising Algorithm for CT Image Using Low-rank Sparse Coding.

We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.

Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks.

This paper tries to separate fine-grained images by jointly learning the encoding parameters and codebooks through low-rank sparse coding (LRSC) with general and class-specific codebook generation. Instead of treating each local feature independently, we encode the local features within a spatial region jointly by LRSC. This ensures that the spatially nearby local features with similar visual characters are encoded by correlated parameters. In this way, we can make the encoded parameters more consistent for fine-grained image representation. Besides, we also learn a general codebook and a number of class-specific codebooks in combination with the encoding scheme. Since images of fine-grained classes are visually similar, the difference is relatively small between the general codebook and each class-specific codebook. We impose sparsity constraints to model this relationship. Moreover, the incoherences with different codebooks and class-specific codebooks are jointly considered. We evaluate the proposed method on several public image data sets. The experimental results show that by learning general and class-specific codebooks with the joint encoding of local features, we are able to model the differences among different fine-grained classes than many other fine-grained image classification methods.