Kernel VA-Files for Nearest Neighbor Search in Large Image Databases

Many data partitioning index methods perform poorly in high dimensional space. The vector approximation file (VA-File) approach overcomes some of the difficulties of high dimensional vector spaces, but cannot be applied when using a kernel distance metric in the data measurement space. This paper introduces a novel KVA-File (kernel VA-File) that extends VAFile to kernel-based retrieval methods. A key observation is that kernel metrics may be non-linear in the input data space but is still linear in an induced feature space. It is this linear invariance in the induced feature space that enables KVA-File to work with kernel metrics. An efficient approach to approximating vectors in an induced feature space is presented with the corresponding upper and lower distance bounds. Thus an effective indexing method is provided for kernel-based image retrieval methods. Experimental results using large image data sets (approximately 100,000 images with 463 dimensions of measurement) validate the efficacy of our method.

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