Improved Deep Hashing With Soft Pairwise Similarity For Multi-label Image Retrieval
2018 · Zheng Zhang, Qin Zou, Yuewei Lin, et al.
Abstract
Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning-based methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is '1' if they share no less than one class label and '0' if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into a percentage based on the normalized semantic labels. Based on the instance similarity, a weighted cross-entropy loss and a minimum mean square err
Authors
(none)
Tags
Stats
Related papers
- Instance-weighted Central Similarity For Multi-label Image Retrieval (2021)0.00
- Deep Class-wise Hashing: Semantics-preserving Hashing Via Class-wise Loss (2018)11.19
- Hierarchy Neighborhood Discriminative Hashing For An Unified View Of Single-label And Multi-label Image Retrieval (2019)0.00
- Improved Deep Classwise Hashing With Centers Similarity Learning For Image Retrieval (2021)5.84
- Deep Discrete Hashing With Self-supervised Pairwise Labels (2017)9.49
- Deep Self-adaptive Hashing For Image Retrieval (2021)8.35
- SSDH: Semi-supervised Deep Hashing For Large Scale Image Retrieval (2016)15.40
- Deep Class-guided Hashing For Multi-label Cross-modal Retrieval (2024)6.20