A Generic Grouping Algorithm and Its Quantitative Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Digital Image Processing
An Evaluation of Color-Spatial Retrieval Techniques for Large Image Databases
Multimedia Tools and Applications
Integrated spatial and feature image query
Multimedia Systems
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Region growing: a new approach
IEEE Transactions on Image Processing
Matching and retrieval based on the vocabulary and grammar of color patterns
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Adaptive perceptual color-texture image segmentation
IEEE Transactions on Image Processing
Development of expert system for extraction of the objects of interest
Expert Systems with Applications: An International Journal
Content-based image retrieval using OWA fuzzy linking histogram
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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A new color image segmentation method is proposed in this paper. The proposed method is based on the human perception that in general human has attention on 3 or 4 major color objects in the image at first. Therefore, to determine the objects, three intensity distributions are constructed by sampling them randomly and sufficiently from three R, G, and B channel images. And three means are computed from three intensity distributions. Next, these steps are repeated many times to obtain three mean distribution sets. Each of these distributions comes to show normal shape based on the central limit theorem. To segment objects, each of the normal distribution is divided into 4 sections according to the standard deviation (section1 below -σ, section 2 between -σ andµ, section 3 between µ andσ, and section 4 overσ). Then sections with similar representative values are merged based on the threshold. This threshold is not chosen as constant but varies based on the difference of representative values of each section to reflect various characteristics for various images. Above merging process is iterated to reduce fine textures such as speckles remained even after the merging. Finally, segmented results of each channel images are combined to obtain a final segmentation result. The performance of the proposed method is evaluated through experiments over some images.