Unsupervised Segmentation of Textured Images by Edge Detection in Multidimensional Feature
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying high level features of texture perception
CVGIP: Graphical Models and Image Processing
Segmentation of Color Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
Experiments in colour texture analysis
Pattern Recognition Letters
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the MPEG-7 standard and of future challenges for visual information analysis
EURASIP Journal on Applied Signal Processing - Image analysis for multimedia interactive services - part I
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
Very Low Bit Rate Semantic Compression of Natural Outdoor Images.
Very Low Bit Rate Semantic Compression of Natural Outdoor Images.
Texture coding using a Wold decomposition model
IEEE Transactions on Image Processing
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Over the past four decades, image compression and object recognition have evolved from pixel-level to region-level processing, and thence to feature-based resolution. For example, compression has progressed from entropy coding of a bit or pixel stream, to transform coding applied to rectangular encoding blocks, to feature-based compression that employs segmentation of isospectral or isotextural regions. Similarly, object recognition has progressed from operations on individual pixel intensities or spectral signatures, to region- or feature-based processing employing segmentation. Recent progress in image compression indicates that significantly decreased bit rate (thus, significantly increased compression ratio) can be obtained by isolating or segmenting, then compressing scene objects, which is called object-based compression or OBC.In this paper, the relationship between object segmentation and compression is presented theoretically, then exemplified in terms of current research in OBC. The relationship between object compression and recognition is also discussed theoretically. Recent work in object recognition is shown to be closely related theoretically to OBC. Two recently-developed paradigms, Muñoz et al.'s region growing algorithm and Campbell et al.'s Quick-Sketch, which support very efficient compression and accurate recognition/retrieval of image objects, are summarized. Additionally. a performance model is given for OBC that isolates space complexity bottlenecks for future enhancement and implementation.