Experiments with a featureless approach to pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Emergent Semantics through Interaction in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Learning Feature Relevance and Similarity Metrics in Image Databases
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Similarity Measure Learning for Image Retrieval Using Feature Subspace Analysis
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
One-class svms for document classification
The Journal of Machine Learning Research
Visualization and User-Modeling for Browsing Personal Photo Libraries
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
MediaMill: exploring news video archives based on learned semantics
Proceedings of the 13th annual ACM international conference on Multimedia
Scenario optimization for interactive category search
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Robust Scene Categorization by Learning Image Statistics in Context
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Learning user queries in multimodal dissimilarity spaces
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Learning similarity measure for natural image retrieval with relevance feedback
IEEE Transactions on Neural Networks
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Combining multimodal preferences for multimedia information retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Deep exploration for experiential image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Dissimilarity representation in multi-feature spaces for image retrieval
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Further results on dissimilarity spaces for hyperspectral images RF-CBIR
Pattern Recognition Letters
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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In this paper, we introduce a new approach 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 a parameterized function of the features. Different from existing techniques, we use relevance feedback to adjust dissimilarity in a dissimilarity space. To create a dissimilarity space, we use Pekalska's method [15]. After the user gives feed-back, we apply active learning with one-class SVM on this space. Results on a Corel dataset of 10000 images and a TrecVid collection of 43907 keyframes show that our proposed approach can improve the retrieval performance over the feature space based approach.