VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional binary search trees used for associative searching
Communications of the ACM
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Training products of experts by minimizing contrastive divergence
Neural Computation
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Cortina: a system for large-scale, content-based web image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
(Un)Reliability of video concept detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Fast solvers and efficient implementations for distance metric learning
Proceedings of the 25th international conference on Machine learning
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deep networks for image retrieval on large-scale databases
MM '08 Proceedings of the 16th ACM international conference on Multimedia
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Compact hashing for mixed image-keyword query over multi-label images
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Image retrieval with query-adaptive hashing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Graph-based semi-supervised learning with multi-modality propagation for large-scale image datasets
Journal of Visual Communication and Image Representation
Dual local consistency hashing with discriminative projections selection
Signal Processing
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With the proliferation of images on the Web, fast search of visually similar images has attracted significant attention. State-of-the-art techniques often embed high-dimensional visual features into low-dimensional Hamming space, where search can be performed in real-time based on Hamming distance of compact binary codes. Unlike traditional metrics (e.g., Euclidean) of raw image features that produce continuous distance, the Hamming distances are discrete integer values. In practice, there are often a large number of images sharing equal Hamming distances to a query, resulting in a critical issue for image search where ranking is very important. In this paper, we propose a novel approach that facilitates query-adaptive ranking for the images with equal Hamming distance. We achieve this goal by firstly offline learning bit weights of the binary codes for a diverse set of predefined semantic concept classes. The weight learning process is formulated as a quadratic programming problem that minimizes intra-class distance while preserving interclass relationship in the original raw image feature space. Query-adaptive weights are then rapidly computed by evaluating the proximity between a query and the concept categories. With the adaptive bit weights, the returned images can be ordered by weighted Hamming distance at a finer-grained binary code level rather than at the original integer Hamming distance level. Experimental results on a Flickr image dataset show clear improvements from our query-adaptive ranking approach.