Lost in quantization: Improving particular object retrieval in large scale image databases

The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local regions of images are characterized using high-dimensional descriptors which are then mapped to "visual words" selected from a discrete vocabulary. This paper explores techniques to map each visual region to a weighted set of words, allowing the inclusion of features which were lost in the quantization stage of previous systems. The set of visual words is obtained by selecting words based on proximity in descriptor space. We describe how this representation may be incorporated into a standard tf-idf architecture, and how spatial verification is modified in the case of this soft-assignment. We evaluate our method on the standard Oxford Buildings dataset, and introduce a new dataset for evaluation. Our results exceed the current state of the art retrieval performance on these datasets, particularly on...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, Andrew Zisserman
• Keywords: Art Retrieval Performance, Computer Vision, CVPR 2008, Query Expansion Suffer, Text Retrieval, Visual Object Retrieval, Visual Words