IEEE Trans Image Process - Sparse representation with kernels.

Tópicos

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Resumo

Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.

Resumo Limpo

recent research shown initi success spars code sc solv mani comput vision task motiv fact kernel trick can captur nonlinear similar featur help find spars represent nonlinear featur propos kernel spars represent ksr essenti ksr spars code techniqu high dimension featur space map implicit map function appli ksr featur code imag classif face recognit kernel matrix approxim specif incorpor ksr spatial pyramid match spm develop ksrspm achiev good perform imag classif moreov ksrbase featur code can shown general effici match kernel extens scbase spm show propos ksr use histogram intersect kernel hik can consid soft assign extens hikbas featur quantiz featur code process besid featur code compar spars code ksr can learn discrimin spars code achiev higher accuraci face recognit moreov ksr can also appli kernel matrix approxim larg scale learn task demonstr robust kernel matrix approxim especi small fraction data use extens experiment result demonstr promis result ksr imag classif face recognit kernel matrix approxim applic prove effect ksr comput vision machin learn task

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