IMAGE SEGMENTATION BASED ON GENETIC ALGORITHM COLOR HISTOGRAM
DESIGN DETAILS
Color features of images are represented by color histograms. These are easy to compute, and are invariant to rotation and translation of image content. However, color histograms have several inherent limitations for the task of image indexing and retrieval. In conventional color histogram (CCH) two colors will be considered totally different if they fall into two different bins even though they might be like each other for human perception. That is, CCH considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interferences such as illumination changes and quantization errors. CCH’s high dimensionality (i.e. the number of histogram bins) requires large computations on histogram comparison. Finally, color histograms do not include any spatial information and are therefore not suitable to support image indexing and retrieval, based on local image contents.
Segmentation involves partitioning an image into a set of homogeneous and meaningful regions, such that the pixels in each partitioned region possess an identical set of properties. Image segmentation is one of the most challenging tasks in image processing and is an important pre-processing step in the problems in image analysis, computer vision, and pattern recognition. In many applications, the quality of final object classification and scene interpretation depends largely on the quality of the segmented output. In segmentation, an image is partitioned into different non-overlapping homogeneous regions, where the homogeneity of a region may be composed based on different criteria such as gray level, color or texture.
This design is based on segmentation scheme based on Genetic Algorithm histogram using Matlab program.
REFERENCES
Reference Paper-1: Color Image Segmentation using Genetic Algorithm
Author’s Name: Megha Sahu and K.M. Bhurchandi
Source: IJCA
Year: 2016
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