International Journal of Computer Vision
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Multidimensional access methods
ACM Computing Surveys (CSUR)
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Information Retrieval
Database Management Systems
A compact and efficient image retrieval approach based on border/interior pixel classification
Proceedings of the eleventh international conference on Information and knowledge management
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Proximity Matching Using Fixed-Queries Trees
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Methods for ranking information retrieval systems without relevance judgments
Proceedings of the 2003 ACM symposium on Applied computing
The Amsterdam Library of Object Images
International Journal of Computer Vision
Re-ranking algorithm using post-retrieval clustering for content-based image retrieval
Information Processing and Management: an International Journal
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of Database Systems (5th Edition)
Fundamentals of Database Systems (5th Edition)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Making colors worth more than a thousand words
Proceedings of the 2008 ACM symposium on Applied computing
Efficient and Flexible Cluster-and-Search for CBIR
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Active reranking for web image search
IEEE Transactions on Image Processing
A similarity measure for indefinite rankings
ACM Transactions on Information Systems (TOIS)
BP-tree: an efficient index for similarity search in high-dimensional metric spaces
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Relevance feedback based on genetic programming for image retrieval
Pattern Recognition Letters
Articulation-invariant representation of non-planar shapes
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
A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
Exploiting contextual spaces for image re-ranking and rank aggregation
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Learning context-sensitive similarity by shortest path propagation
Pattern Recognition
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
Affinity learning on a tensor product graph with applications to shape and image retrieval
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Affinity Learning with Diffusion on Tensor Product Graph
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Content-based Image Retrieval (CBIR) systems consider only a pairwise analysis, i.e., they measure the similarity between pairs of images, ignoring the rich information encoded in the relations among several images. However, the user perception usually considers the query specification and responses in a given context. In this scenario, re-ranking methods have been proposed to exploit the contextual information and, hence, improve the effectiveness of CBIR systems. Besides the effectiveness, the usefulness of those systems in real-world applications also depends on the efficiency and scalability of the retrieval process, imposing a great challenge to the re-ranking approaches, once they usually require the computation of distances among all the images of a given collection. In this paper, we present a novel approach for the re-ranking problem. It relies on the similarity of top-k lists produced by efficient indexing structures, instead of using distance information from the entire collection. Extensive experiments were conducted on a large image collection, using several indexing structures. Results from a rigorous experimental protocol show that the proposed method can obtain significant effectiveness gains (up to 12.19% better) and, at the same time, improve considerably the efficiency (up to 73.11% faster). In addition, our technique scales up very well, which makes it suitable for large collections.