Fusing multi-cues description for partial-duplicate image retrieval

• Published in: Journal of visual communication and image representation Vol. 25; no. 7; pp. 1726 - 1731
• Main Authors: Yan, Chenggang Clarence, Li, Liang, Wang, Zhan, Yin, Jian, Shi, Hailong, Jiang, Shuqiang, Huang, Qingming
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2014
• Subjects: Computational efficiency; Local Self-Similarity Descriptor; Mathematical models; Multi-cues; Partial-duplicate image retrieval; Pyramids; Representations; Retrieval; Self-similarity; Spatial pyramid; Visual; Multi-cues; Partial-duplicate image retrieval; Local Self-Similarity Descriptor; Spatial pyramid
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2014.06.005

In traditional image retrieval, images are commonly represented using Bag-of-visual-Words (BoW) built from image local features. However, the lack of spatial and structural information suppresses its performance in applications. In this paper, we introduce a multi-cues description by fusing structural, content and spatial information for partial-duplicate image retrieval. Firstly, we propose a rotation-invariant Local Self-Similarity Descriptor (LSSD), which captures the internal structural layouts in the local textural self-similar regions around interest points. Then, based on the spatial pyramid model, we make use of both LSSD and SIFT to construct an image representation with multi-cues. Finally, we formulate the Semi-Relative Entropy as the distance metric. Comparison experiments with state-of-the-art methods on four popular databases show the efficiency and effectiveness of our approach.