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Deep Collaborative Graph Hashing for Discriminative Image Retrieval by Zheng Zhang; Jianning Wang; Lei Zhu; Yadan Luo; Guangming Lu is a Computer Science article available to read on EtoBox.
What is Deep Collaborative Graph Hashing for Discriminative Image Retrieval about?
The most striking success of deep hashing for large-scale image retrieval benefits from its powerful discriminative representation of deep learning and the attractive computational efficiency of compact hash code learning. Most existing deep semantic-preserving hashing regard the available semantic labels as the ground truth for classification or transform them into prevalent pairwise similarities. However, such strategies fail to capture the interactive correlations between the visual semantics embedded in images and the given category-level labels. Moreover, they utilize the fixed piecewise or pairwise semantics as the optimization objectives, which suffers from the limited flexibility on semantic representation and adaptive knowledge communication in hash code learning. In this paper, we propose a novel Deep Collaborative Graph Hashing (DCGH), which collectively considers multi-level semantic embeddings, latent common space construction, and intrinsic structure mining in discriminative hash codes learning, for large-scale image retrieval. To the best of our knowledge, this is the first collaborative graph hashing for image retrieval. Specifically, instead of using the convention
Who reads Deep Collaborative Graph Hashing for Discriminative Image Retrieval?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Zheng Zhang; Jianning Wang; Lei Zhu; Yadan Luo; Guangming Lu
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Computer Science (Physical Sciences)