Extensive operators in partition lattices for image sequence analysis
Signal Processing - Video segmentation for content-based processing manipulation
Finding salient regions in images: nonparametric clustering for image segmentation and grouping
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Understanding Digital Color
Contour Continuity in Region Based Image Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Integration of Regions and Contours for Object Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Global parallel index for multi-processors database systems
Information Sciences: an International Journal
Efficient image indexing and retrieval over mobile devices
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Hierarchical semantic-based index for ad hoc image retrieval
Journal of Mobile Multimedia
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
Region-based representations of image and video: segmentation tools for multimedia services
IEEE Transactions on Circuits and Systems for Video Technology
Region analysis of abdominal CT scans using image partition forests
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
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In this paper, we present a novel algorithm for representing image content by constructing a hierarchy of semantic image regions, called a Semantic Segmentation Tree (SSeg-tree). First, the hill-manipulation algorithm divides an image into several visually coherent segments (small regions), which form the leaves of the SSeg-tree. Then, the method groups these segments based on well-defined spatio-visual grouping rules to produce bigger and more semantic regions, which form the intermediate nodes of the SSeg-tree. The SSeg-tree is a region-based description of the image semantic content that could be useful in many applications such as CBIR and filtering unwanted objects.