Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Emergent Semantics through Interaction in Image Databases
IEEE Transactions on Knowledge and Data Engineering
SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data
IEEE Transactions on Knowledge and Data Engineering
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Context-dependent segmentation and matching in image databases
Computer Vision and Image Understanding
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
IEEE Transactions on Image Processing
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Interest in digital images content has increased enormously over the last few years. Segmentation algorithms are used to extract region-based descriptions of an image and provide an input to higher level image processing, e.g. for content-based image retrieval (CBIR). Frequently it is difficult even for a user to single out representative regions or its combinations. Partitions and coverings of an image and range of gray levels (colors) are ones of principal constructive objects for an analysis. Their processing creates the necessary prerequisites to synthesize new features for CBIR and to consider redundancy and deficiency of information as well as its multiple meaning for totally correct and complete segmentation of complex scenes. The paper is dedicated to theoretical and experimental exploration of coverings and partitions produced by multithresholding segmentation.