Unsupervised Segmentation of Color-Texture Regions in Images and Video

By Yining Deng, Bangalore S. Manjunath. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001

1,563 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7ff4bc32bbdeb84c403a0535b244d468b352ce06

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In Computer Vision and Pattern Recognition

Citations in the corpus

Segmentation of Natural Images by Texture and Boundary Compression (2010, neutral): “The proposed region-merging method has been widely used by other image segmentation algorithms (Haralick and Shapiro 1985; Tremeau and Borel 1997; Deng and Manjunath 2001).”
Semantic object classes in video: A high-definition ground truth database (2009, methodology): “In the context of video based object analysis, many advanced techniques have been proposed for object segmentation (Marcotegui et al., 1999; Deng and Manjunath, 2001; Patras et al., 2003; Wang et al., 2005; Agarwala et al., 2004).”
A Formal Study of Shot Boundary Detection (2007, methodology): “Various approaches, such as JSEG [ 66 ], Mean Shift [67], and graph partition model [53], [54], [68], have been proposed.”
Image retrieval: Ideas, influences, and trends of the new age (2008, methodology): “An unsupervised approach for segmentation of images containing homogeneous color/texture regions has been proposed in [Deng and Manjunath 2001].”
Recognition of Multiple-Food Images by Detecting Candidate Regions (2012, methodology): “The proposed method detects candidate regions with several methods including Felzenszwalb’s deformable part model (DPM) [1], a circle detector and the JSEG region segmentation [6].”
Seeded region growing: an extensive and comparative study (2005, methodology): “However, automatic semantic image segmentation is an ill-defined problem because semantic objects do not usually correspond to homogeneous regions in color or texture (Deng and Manjunath, 2001).”
Concept-Based Video Retrieval (2009, background): “Shape: Alternatively, a key frame can be segmented by grouping pixels in the image based on a homogeneity criterion on color, texture, or both [32] [54] [89], or by connecting edge lines [65].”
A survey of content-based image retrieval with high-level semantics (2007, background): “Similarity between two images is defined based on region features.”
Segmentation using superpixels: A bipartite graph partitioning approach (2012, methodology): “We compare the average scores of SAS and the ten benchmark algorithms, Ncut [26], Mean Shift [4], FH [10], JSEG [6], Multi-scale Ncut (MNcut) [5], Normalized Tree Partitioning (NTP) [28], Saliency Driven Total Variation (SDTV) [9], Texture and Boundary Encoding-based Segmentation (TBES) [21], Ultram”
A Review on Image Feature Extraction and Representation Techniques (2013, methodology): “Figure 4 illustrates the regions obtained by these three methods, where (a) shows the original image, (b) gives the image segmented by a regular grid, (c) provides the image segmented by the JSEG [25], and (d) outlines the salient regions detected by the Difference of Gaussian (DoG) [26] detector .”
Wavelet-based multiscale texture segmentation: Application to stromal compartment characterization on virtual slides (2010, methodology): “Based texture segmentation methods (such as JSEG criterion (Deng and Manjunath, 2001) or active region based segmentation (Lecellier et al., 2006)) were not able to provide good results of segmentation.”
Unsupervised extraction of visual attention objects in color images (2006, methodology): “Our work incorporates visual attention with seeded object/ region growing [2], [ 10 ] to extract the attention object.”
A methodological approach to the classification of dermoscopy images (2007, methodology): “The method is based on the JSEG algorithm [19] and included three main phases: preprocessing , segmentation, and postprocessing.”