Developing AContent-Based Image Retrieval System Using Segmentation and Color Feature.

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Addis Ababa University


In this thesis, a Content-Based Image Retrieval (CBIR) system that is based on segmentation and color feature is presented. CBIR helps to organize and retrieve digital images using their visual content from large databases. The CBIR system proposed in this work consists of segmentation, feature extraction and similarity measuring techniques to store and to retrieve the images. The segmentation is done using the K-Means clustering algorithm. Unlike existing systems, the segmentation used is unreliable that the image is divided into many small regions that are far from representing semantic objects. Feature extraction is done for each region of the image. A region is represented by its average color and number of pixels. The average color is calculated for each channel of the RGB color space. An image is stored in the database as a collection of regions. The similarity of two images is based on the similarity of their regions. Two different similarity measures are proposed in this work. One is based on count of similar regions and the other measure is based on the sum of pixels of the similar regions. Region similarity is based on the Euclidean distance between their average colors and the number of pixels of the regions. Two images that have more number of similar regions are said to be more similar. The proposed system is compared with some existing systems like Earth Mover’s Distance (EMD) and SIMPLIcity using three parameters: precision, average rank and standard deviation of the ranks. The performance of the proposed system is promising and found to be better than EMD. However, the system didn’t perform well compared to SIMPLIcity, owing to the fact that SIMPLIcity uses color, shape and texture for the feature extraction technique whereas the proposed system uses only the color feature.



Color Feature