Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Introduction to Information Retrieval
Introduction to Information Retrieval
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building contextual visual vocabulary for large-scale image applications
Proceedings of the international conference on Multimedia
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Adding Affine Invariant Geometric Constraint for Partial-Duplicate Image Retrieval
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual weighting for vocabulary tree based image retrieval
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Aggregating Local Image Descriptors into Compact Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Many image retrieval and search systems take visual words as the key to represent images. Extracted feature descriptors are assigned to best matching visual words in the vocabulary. However, the visual word based retrieval method is not satisfactory in real applications as the descriptive ability of visual vocabulary is not strong enough to describe image local features. In this paper, we investigate on the problem of the descriptive ability of visual vocabulary. There are mainly two contributions in this paper. Firstly, we make a comprehensive analysis on the retrieval performance of standard bag-of-features (BOF) method by using different codebooks. The codebook is generated on one dataset and used for retrieval on the same dataset and on different datasets, which is called ''homology codebook'' and ''non-homology codebook'', respectively. Experimental results show that there exists performance drop when a non-homology codebook is used for retrieval on one dataset, compared to the homology codebook. This phenomena is usually neglected by most of the retrieval tasks and systems. Secondly, in order to abate the influence of non-homology codebook, a weighting based method is proposed to balance the descriptive ability of codebook for different datasets. Experimental evaluations show that the proposed method outperforms standard BOF method, and it can prevent the performance drop to some extent when non-homology codebooks are used.