Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Exploring the Nature and Variants of Relevance Feedback
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Learning in Content-Based Image Retrieval
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Representations, feature extraction, matching and relevance feedback for sketch retrieval
Representations, feature extraction, matching and relevance feedback for sketch retrieval
Enumeration of cospectral graphs
European Journal of Combinatorics - Special issue on algebraic combinatorics: in memory of J.J. Seidel
Sketch Retrieval Based on Spatial Relations
CGIV '05 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Relevance feedback for sketch retrieval based on linear programming classification
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Partial matching of garment panel shapes with dynamic sketching design
Proceedings of the 1st Augmented Human International Conference
Expert Systems with Applications: An International Journal
Understanding, Manipulating and Searching Hand-Drawn Concept Maps
ACM Transactions on Intelligent Systems and Technology (TIST)
A spatio-temporal pyramid matching for video retrieval
Computer Vision and Image Understanding
Fisher kernel based relevance feedback for multimodal video retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
A performance evaluation of gradient field HOG descriptor for sketch based image retrieval
Computer Vision and Image Understanding
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This paper proposes an effective approach for content-based sketch retrieval. It addresses three characteristics as follows. Firstly, both structural relations and global shape descriptors are combined to represent sketch content. Secondly, feature weighting and combination are performed to obtain a reasonable mechanism for similarity calculation. Finally, relevance feedback based on biased SVM (BSVM) algorithm is employed to capture user's query interests online and thus improve retrieval performance. Experiments prove the effectiveness of our proposed method in sketch retrieval.