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Learning compact representations for large scale image search

Abstract : This thesis addresses the problem of large-scale image search. To tackle image search at large scale, it is required to encode images with compact representations which can be efficiently employed to compare images meaningfully. Obtaining such compact representation can be done either by compressing effective high dimensional representations or by learning compact representations in an end-to-end manner. The work in this thesis explores and advances in both of these directions. In our first contribution, we extend structured vector quantization approaches such as Product Quantization by proposing a weighted codeword sum representation. We test and verify the benefits of our approach for approximate nearest neighbor search on local and global image features which is an important way to approach large scale image search. Learning compact representation for image search recently got a lot of attention with various deep hashing based approaches being proposed. In such approaches, deep convolutional neural networks are learned to encode images into compact binary codes. In this thesis we propose a deep supervised learning approach for structured binary representation which is a reminiscent of structured vector quantization approaches such as PQ. Our approach benefits from asymmetric search over deep hashing approaches and gives a clear improvement for search accuracy at the same bit-rate. Inverted index is another important part of large scale search system apart from the compact representation. To this end, we extend our ideas for supervised compact representation learning for building inverted indexes. In this work we approach inverted indexing with supervised deep learning and make an attempt to unify the learning of inverted index and compact representation. We thoroughly evaluate all the proposed methods on various publicly available datasets. Our methods either outperform, or are competitive with the state-of-the-art.
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Submitted on : Friday, November 30, 2018 - 5:11:17 PM
Last modification on : Friday, April 8, 2022 - 4:04:02 PM
Long-term archiving on: : Friday, March 1, 2019 - 3:25:36 PM


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  • HAL Id : tel-01889405, version 2


Himalaya Jain. Learning compact representations for large scale image search. Artificial Intelligence [cs.AI]. Université Rennes 1, 2018. English. ⟨NNT : 2018REN1S027⟩. ⟨tel-01889405v2⟩



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