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Natural Image Segmentation and Object Recognition using ACA and Steerable Filter based on Color-Texture Features
(2011-12-30T09:14:39Z) Nilima, Kulkarni; Rajurkar, A.M.
An image segmentation algorithm that is based on low-level features for color and texture is presented. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. We have tried to recognize objects in images. We used color values for object recognition. The images are assumed to be of relatively low resolution and may be degraded compressed