Tree Quantization for Large-Scale Similarity Search and Classification

Artem Babenko, Victor Lempitsky; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4240-4248

Abstract


We propose a new vector encoding scheme (tree quantization) that obtains lossy compact codes for high-dimensional vectors via tree-based dynamic programming. Similarly to several previous schemes such as product quantization, these codes correspond to codeword numbers within multiple codebooks. We propose an integer programming-based optimization that jointly recovers the coding tree structure and the codebooks by minimizing the compression error on a training dataset. In the experiments with diverse visual descriptors (SIFT, neural codes, Fisher vectors), tree quantization is shown to combine fast encoding and state-of-the-art accuracy in terms of the compression error, the retrieval performance, and the image classification error.

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[bibtex]
@InProceedings{Babenko_2015_CVPR,
author = {Babenko, Artem and Lempitsky, Victor},
title = {Tree Quantization for Large-Scale Similarity Search and Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}