Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Content-Based Image Visualization
IV '00 Proceedings of the International Conference on Information Visualisation
Image Content-Based Retrieval Using Chromaticity Moments
IEEE Transactions on Knowledge and Data Engineering
Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Perceptual Shape-Based Natural Image Representation and Retrieval
ICSC '07 Proceedings of the International Conference on Semantic Computing
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Interactive access to large image collections using similarity-based visualization
Journal of Visual Languages and Computing
Content-Based image retrieval using perceptual shape features
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
International Journal of Computational Vision and Robotics
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CBIR has been an active topic for more than one decade. Current systems still lack in flexibility and accuracy because of semantic gap between image's feature-level and semantic-level representations. Although many techniques have been developed for automatic or semi-automatic retrieval (e.g. interactive browsing, relevance feedback (RF)), issues about how to find suitable features and how to measure the image content still remain. It has been a challenging task to choose sound features for coding image content properly. This requires intensive interactive effort for discovering useful regularities between features and content semantics. In this paper, we present an interactive visualization system for supporting feature investigations. It allows users to choose different features, feature combinations, and representations for testing their impacts on measuring content-semantics. The experiments demonstrate how various perceptual edge features and groupings are interactively handled for retrieval measures. The system can be extended to include more features.