Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval

2019/01/31 by Xuefei Zhe, Zhang, Ming, Le Ou-Yang +6 · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques

paper · pdf · doi:10.48550/arxiv.1901.11259

openalex publication_date 2019/01/31 · openalex created_date 2019/02/21 · openalex updated_date 2026/07/28

Abstract

Deep hashing models have been proposed as an efficient method for large-scale similarity search. However, most existing deep hashing methods only utilize fine-level labels for training while ignoring the natural semantic hierarchy structure. This paper presents an effective method that preserves the classwise similarity of full-level semantic hierarchy for large-scale image retrieval. Experiments on two benchmark datasets show that our method helps improve the fine-level retrieval performance. Moreover, with the help of the semantic hierarchy, it can produce significantly better binary codes for hierarchical retrieval, which indicates its potential of providing more user-desired retrieval results.

Citations

• Cumulated gain-based evaluation of IR techniques
• ImageNet Large Scale Visual Recognition Challenge
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• Supervised Deep Hashing for Hierarchical Labeled Data
• Learning Multiple Layers of Features from Tiny Images

Cited by

• Learning to hash with semantic similarity metrics and empirical KL divergence

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