Image retrieval based on kernel trick and iterative quantization

This paper proposes a novel algorithm for solving the problem of data linear inseparable and low-accuracy in the image retrieval field. In order to get hash codes, the algorithm takes account of kernel trick and iterative quantization. First, the kernel trick is used to map the image data from low-dimension into high-dimension cleverly. In this way the data become linearly separable, and the trained hash codes are proved to be effective. Second, in the process of training the hash function, iterative quantization is used to quantize the image data to the closest hash codes. Finally, the quantitative error is minimized, and the hash codes are generated for image retrieval. Experimental results show that it certainly outperforms other compared hashing algorithms on two image benchmarks.