Contextual weighting for vocabulary tree based image retrieval

In this paper we address the problem of image retrieval from millions of database images. We improve the vocabulary tree based approach by introducing contextual weighting of local features in both descriptor and spatial domains. Specifically, we propose to incorporate efficient statistics of neighb...

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Bibliographic Details
Main Authors: Xiaoyu Wang, Ming Yang, Cour, T., Shenghuo Zhu, Kai Yu, Han, T. X.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Image retrieval
Indexes
Quantization
Visualization
Vocabulary
Online Access:Request full text
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Description
Summary:In this paper we address the problem of image retrieval from millions of database images. We improve the vocabulary tree based approach by introducing contextual weighting of local features in both descriptor and spatial domains. Specifically, we propose to incorporate efficient statistics of neighbor descriptors both on the vocabulary tree and in the image spatial domain into the retrieval. These contextual cues substantially enhance the discriminative power of individual local features with very small computational overhead. We have conducted extensive experiments on benchmark datasets, i.e., the UKbench, Holidays, and our new Mobile dataset, which show that our method reaches state-of-the-art performance with much less computation. Furthermore, the proposed method demonstrates excellent scalability in terms of both retrieval accuracy and efficiency on large-scale experiments using 1.26 million images from the ImageNet database as distractors.
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2011.6126244