International Journal of Computer Vision
Information Retrieval
A compact and efficient image retrieval approach based on border/interior pixel classification
Proceedings of the eleventh international conference on Information and knowledge management
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
BAS: a perceptual shape descriptor based on the beam angle statistics
Pattern Recognition Letters
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Color Co-occurence Descriptors for Querying-by-Example
MMM '98 Proceedings of the 1998 Conference on MultiMedia Modeling
Re-ranking algorithm using post-retrieval clustering for content-based image retrieval
Information Processing and Management: an International Journal
Regularizing ad hoc retrieval scores
Proceedings of the 14th ACM international conference on Information and knowledge management
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Document re-ranking using cluster validation and label propagation
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global ranking by exploiting user clicks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to rank for content-based image retrieval
Proceedings of the international conference on Multimedia information retrieval
Co-transduction for shape retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Balancing deformability and discriminability for shape matching
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Exploiting contextual information for image re-ranking
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Information Sciences: an International Journal
A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
Content-based facial image retrieval using constrained independent component analysis
Information Sciences: an International Journal
Image re-ranking and rank aggregation based on similarity of ranked lists
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Exploiting clustering approaches for image re-ranking
Journal of Visual Languages and Computing
Beyond pairwise shape similarity analysis
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Halfway through the semantic gap: Prosemantic features for image retrieval
Information Sciences: an International Journal
Pattern Recognition Letters
Image and Vision Computing
| Hi-index | 0.07 |
In Content-based Image Retrieval (CBIR) systems, accurately ranking collection images is of great relevance. Users are interested in the returned images placed at the first positions, which usually are the most relevant ones. Commonly, image content descriptors are used to compute ranked lists in CBIR systems. In general, these systems perform only pairwise image analysis, that is, compute similarity measures considering only pairs of images, ignoring the rich information encoded in the relations among several images. This paper presents a novel re-ranking approach used to improve the effectiveness of CBIR tasks by exploring relations among images. In our approach, a recommendation-based strategy is combined with a clustering method. Both exploit contextual information encoded in ranked lists computed by CBIR systems. We conduct several experiments to evaluate the proposed method. Our experiments consider shape, color, and texture descriptors and comparisons with other post-processing methods. Experimental results demonstrate the effectiveness of our method.