Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
This work addresses the need for more user-desired retrieval results in image search systems by incorporating semantic hierarchy, though it is incremental as it builds on existing deep hashing methods.
The paper tackles the problem of large-scale image retrieval by preserving semantic hierarchy in deep hashing, resulting in improved fine-level retrieval performance and significantly better binary codes for hierarchical retrieval on benchmark datasets.
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.