Rank-preserving and unsupervised hash learning from auxiliary contextual cues
Journal
ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014
Pages
399-402
Date Issued
2014
Author(s)
Kuo Y.-H.
Abstract
Due to the rapid growth of multimedia capture devices, the needs of efficient large-scale multimedia retrieval systems have attracted much attention in different research communities. Recent work shows that hash-based approaches (e.g., LSH - locality-sensitive hashing) can provide efficient and effective retrieval results for approximate nearest neighbor (ANN) problem. Several hash-based learning methods have been proposed to generate more compact binary codes; however, most of them attempt to preserve the similarity score or class (label) information. In the content-based image retrieval (CBIR) tasks, the rank of the search results is also an important information. For web-scale image search, supervised annotations are generally not available; nevertheless, we can easily derive auxiliary semantic cues from the ranking results by different modalities such as keyword search, GPS, tags, where such rankings are easily available. In this work, we propose to preserve the ranking information by utilizing the rank order to learn the hashing functions. Experimental results show that the proposed method is comparable to the state-of-the-art learning-based approaches and can be extended to other existing methods. Moreover, we can further improve the retrieval accuracy by incorporating auxiliary ranking information. Copyright 2014 ACM.
Subjects
Algorithms; Experimentation; Performance
SDGs
Other Subjects
Algorithms; Search engines; Approximate nearest neighbors (ANN); Content-Based Image Retrieval; Experimentation; Learning-based approach; Locality sensitive hashing; Multimedia Retrieval; Performance; Research communities; Content based retrieval
Type
conference paper