Annotating historical archives of images
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Two Step Relevance Feedback for Semantic Disambiguation in Image Retrieval
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
Foundations and Trends in Information Retrieval
Exposing parameters of a trained dynamic model for interactive music creation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Content based image retrieval using unclean positive examples
IEEE Transactions on Image Processing
Mean shift feature space warping for relevance feedback
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Relevance feedback strategies for artistic image collections tagging
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Feature space warping relevance feedback with transductive learning
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
SMI 2013: Grouping real functions defined on 3D surfaces
Computers and Graphics
Learning kernels on extended Reeb graphs for 3d shape classification and retrieval
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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In this paper, we argue to learn dissimilarity for interactive search in content based image retrieval. In literature, dissimilarity is often learned via the feature space by feature selection, feature weighting or by adjusting the parameters of a function of the features. Other than existing techniques, we use feedback to adjust the dissimilarity space independent of feature space. This has the great advantage that it manipulates dissimilarity directly. To create a dissimilarity space, we use the method proposed by Pekalska and Duin, selecting a set of images called prototypes and computing distances to those prototypes for all images in the collection. After the user gives feedback, we apply active learning with a one-class support vector machine to decide the movement of images such that relevant images stay close together while irrelevant ones are pushed away (the work of Guo ). The dissimilarity space is then adjusted accordingly. Results on a Corel dataset of 10000 images and a TrecVid collection of 43907 keyframes show that our proposed approach is not only intuitive, it also significantly improves the retrieval performance.