Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks
Cluster Computing
Unsupervised Texture Segmentation by Dominant Sets and Game Dynamics
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Dominant Sets and Hierarchical Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Dominant Sets and Pairwise Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new graph-theoretic approach to clustering and segmentation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Spatio-temporal segmentation using dominant sets
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Image and collateral text in support of auto-annotation and sentiment analysis
TextGraphs-5 Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing
Fast population game dynamics for dominant sets and other quadratic optimization problems
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Video scene detection using graph-based representations
Image Communication
Graph-based quadratic optimization: A fast evolutionary approach
Computer Vision and Image Understanding
Dominant sets based movie scene detection
Signal Processing
Towards information-theoretic K-means clustering for image indexing
Signal Processing
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In image retrieval algorithms, retrieval is according to feature similarities with respect to the query, ignoring the similarities among images in database. To use the feature similarities information, this paper presents an application of dominant set clustering (DSC) to image retrieval system. Combining the low-level visual features and high-level concepts, the proposed approach fully explores the similarities among images in database using DSC and optimizes the relevance feedback results from traditional image retrieval system by clustering the similar images. To test its retrieval performances, we presented an image retrieval system using the memorized support vector machine (SVM) relevance feedback. The results of experiments on the images from Corel Image Database show that the proposed approach can greatly improve the efficiency and performances of learning machine, as well as the convergence to user's retrieval concept. Comparisons on retrieval precision, total feedback time of method with and without DSC were also made, which indicated an improvement by 6.79% over the average precision and less total relevance feedback times after using DSC.