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
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Adaptive relevance feedback based on Bayesian inference for image retrieval
Signal Processing - Special section on content-based image and video retrieval
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
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Supervised Learning of Quantizer Codebooks by Information Loss Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantics-preserving bag-of-words models for efficient image annotation
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Multimedia
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Web and Personal Image Annotation by Mining Label Correlation With Relaxed Visual Graph Embedding
IEEE Transactions on Image Processing
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Given the explosive growth of the Web images, image search plays an increasingly important role in our daily lives. The visual representation of image is the fundamental factor to the quality of content-based image search. Recently, bag-of-visual word model has been widely used for image representation and has demonstrated promising performance in many applications. In the bag-of-visual-word model, the codebook/visual vocabulary plays a crucial role. The conventional codebook, generated via unsupervised clustering approaches, does not embed the labeling information of images and therefore has less discriminative ability. Although some research has been conducted to construct codebooks with the labeling information considered, very few attempts have been made to exploit manifold geometry of the local feature space to improve codebook discriminative ability. In this paper, we propose a novel discriminative codebook learning method by introducing the subspace learning in codebook construction and leveraging its power to find a contextual local descriptor subspace to capture the discriminative information. The discriminative codebook construction and contextual subspace learning are formulated as an optimization problem and can be learned simultaneously. The effectiveness of the proposed method is evaluated through visual reranking experiments conducted on two real Web image search datasets.