Deep Supervised Hashing with Spherical Embedding
| Autor: | Gianfranco Doretto, Quinn Jones, Stanislav Pidhorskyi, Saeid Motiian, Donald A. Adjeroh |
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| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Nearest neighbor search Quantization (signal processing) Hash function 02 engineering and technology 010501 environmental sciences 01 natural sciences Triplet loss Prior probability 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Binary code Artificial intelligence business Image retrieval Algorithm 0105 earth and related environmental sciences |
| Zdroj: | Computer Vision – ACCV 2018 ISBN: 9783030208691 ACCV (4) |
| DOI: | 10.1007/978-3-030-20870-7_26 |
| Popis: | Deep hashing approaches are widely applied to approximate nearest neighbor search for large-scale image retrieval. We propose Spherical Deep Supervised Hashing (SDSH), a new supervised deep hashing approach to learn compact binary codes. The goal of SDSH is to go beyond learning similarity preserving codes, by encouraging them to also be balanced and to maximize the mean average precision. This is enabled by advocating the use of a different relaxation method, allowing the learning of a spherical embedding, which overcomes the challenge of maintaining the learning problem well-posed without the need to add extra binarizing priors. This allows the formulation of a general triplet loss framework, with the introduction of the spring loss for learning balanced codes, and of the ability to learn an embedding quantization that maximizes the mean average precision. Extensive experiments demonstrate that the approach compares favorably with the state-of-the-art while providing significant performance increase at more compact code sizes. |
| Databáze: | OpenAIRE |
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