Integrated Region-Based Image Retrieval
Integrated Region-Based Image Retrieval
A Model-Based Approach to Semantic-Based Retrieval of Visual Information
SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Approximate Viterbi decoding for 2D-hidden Markov models
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Connected and degraded text recognition using planar hidden Markov models
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
IEEE Transactions on Neural Networks
A novel rotationally invariant region-based hidden Markov model for efficient 3-D image segmentation
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
3D medical volume segmentation using hybrid multiresolution statistical approaches
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
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Segmenting an image into semantically meaningful parts is a fundamental and challenging task in image analysis and scene understanding problems. These systems are of key importance for the new content based applications like object-based image and video compression. Semantic segmentation can be said to emulate the cognitive task performed by the human visual system (HVS) to decide what one "sees", and relies on a priori assumptions. In this paper, we investigate how this prior information can be modeled by learning the local and global context in images by using a multidimensional hidden Markov model. We describe the theory of the model and present experiments conducted on a set of annotated news videos.