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We consider the image classification problem via kernel collaborativerepresentation classification with locality constrained dictionary (KCRC-LCD).Specifically, we propose a kernel collaborative representation classification(KCRC) approach in which kernel method is used to improve the discriminationability of collaborative representation classification (CRC). We then measurethe similarities between the query and atoms in the global dictionary in orderto construct a locality constrained dictionary (LCD) for KCRC. In addition, wediscuss several similarity measure approaches in LCD and further present asimple yet effective unified similarity measure whose superiority is validatedin experiments. There are several appealing aspects associated with LCD. First,LCD can be nicely incorporated under the framework of KCRC. The LCD similaritymeasure can be kernelized under KCRC, which theoretically links CRC and LCDunder the kernel method. Second, KCRC-LCD becomes more scalable to both thetraining set size and the feature dimension. Example shows that KCRC is able toperfectly classify data with certain distribution, while conventional CRC failscompletely. Comprehensive experiments on many public datasets also show thatKCRC-LCD is a robust discriminative classifier with both excellent performanceand good scalability, being comparable or outperforming many otherstate-of-the-art approaches.

KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization