ISSN : 2319-7323


Open Access


Title : Color Image Segmentation and Recognition based on Shape and Color Features
Authors : Rajivkumar Mente, B. V. Dhandra, Gururaj Mukarambi
Keywords : Segmentation, Clustering, L*a*b, Color space, Eccentric ity, kNN, Canny Edge Detection
Issue Date : January 2014
Abstract : Recent advances in computer technology have made it possible to create databases for large number of images. A major approach directed towards achieving CBIR is the use of low-level visual features of the image data to segment, index and retrieve relevant images from the image database. Segmentation is the partition of a digital image into regions to simplify the image representation into someth ing that is more meaningful and easier to analyze. Color based segmentation is significantly affected by the choice of color space. In different color spaces, the L*a*b color space is a better representation of the color content of an image. In this paper the L*a*b color space and K-means algorithm is used for segmentation of color images. Shape description or representation is an important issue both in object recognition and classification. After segmentation this paper focuses on the shape descriptor-eccentricity and color features for achieving efficient and effective retrieval performance. The proposed method is applied to an image database containing 2600 fruit images.
Page(s) : 51-56
ISSN : 2319-7323
Source : Vol. 3, No.1