binarycode learning has recently been emerging topic in large-scale cross-modality retrieval. It aims to map features from multiple modalities into a common Hamming space, where the cross-modality similarity can be a...
详细信息
ISBN:
(纸本)9781538604571
binarycode learning has recently been emerging topic in large-scale cross-modality retrieval. It aims to map features from multiple modalities into a common Hamming space, where the cross-modality similarity can be approximated efficiently via Hamming distance. To this end, most existing works learn binarycodes directly from data instances in multiple modalities, which preserve both intra-and inter-modal similarities respectively. Few methods consider to preserve the "fusion similarity" among multi-modal instances instead, which can explicitly capture their heterogeneous correlation in cross-modality retrieval. In this paper, we propose a hashing scheme, termed Fusion Similarity Hashing (FSH), which explicitly embeds the graphbased fusion similarity across modalities into a common Hamming space. Inspired by the "fusion by diffusion", our core idea is to construct an undirected asymmetric graph to model the fusion similarity among different modalities, upon which a graph hashing scheme with alternating optimization is introduced to learn binarycodes that embeds such fusion similarity. Quantitative evaluations on three widely used benchmarks, i.e., UCI Handwritten Digit, MIR-Flickr25K and NUS-WIDE, demonstrate that the proposed FSH approach can achieve superior performance over the state-of-the-art methods.
暂无评论