Embedding spatial context information into inverted filefor large-scale image retrieval
Proceedings of the 20th ACM international conference on Multimedia
Randomized spatial partition for scene recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Towards exhaustive pairwise matching in large image collections
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
SIFT match verification by geometric coding for large-scale partial-duplicate web image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Robust and accurate mobile visual localization and its applications
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
Context-aware Discriminative Vocabulary Tree Learning for mobile landmark recognition
Digital Signal Processing
Spatially aware feature selection and weighting for object retrieval
Image and Vision Computing
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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.