Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval

Scalable image retrieval systems usually involve hierarchical quantization of local image descriptors, which produces a visual vocabulary for inverted indexing of images. Although hierarchical quantization has the merit of retrieval efficiency, the resulting visual vocabulary representation usually faces two crucial problems: (1) hierarchical quantization errors and biases in the generation of “visual words"; (2) the model cannot adapt to database variance. In this paper, we describe an unsupervised optimization strategy in generating the hierarchy structure of visual vocabulary, which produces a more effective and adaptive retrieval model for large-scale search. We adopt a novel Density-based Metric Learning (DML) algorithm, which corrects word quantization bias without supervision in hierarchy optimization, based on which we present a hierarchical rejection chain for efficient online search based on the vocabulary hierarchy. We also discovered that by hierarchy optimization, e...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Rongrong Ji (Harbin Institute of Technology), Xing Xie (Microsoft Research Asia), Hongxun Yao (Harbin Institute of Technology), Wei-Ying Ma (Microsoft Research Asia)
• Keywords: Computer Vision, CVPR 2009