CONTENT BASED IMAGE RETRIEVAL SYSTEM USING WAVELET FEATURE EXTRACTION BASED UPON HSV PROCESS

Abstract

The project proposes the image retrieval technique based on image contents using different wavelets. The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and web image classification and searching. Content-based Image Retrieval (CBIR) technology overcomes the defects of traditional text-based image retrieval technology, such as heavy workload and strong subjectivity. It makes full use of image content features (color, texture, shape, etc.), which are analyzed and extracted automatically by computer to achieve the effective retrieval Using a single feature for image retrieval cannot be a good solution for the accuracy and efficiency. The images format in the database is RGB format and due to color changes of the image affected by illumination especially for outdoor image acquisition, so RGB model gives different values in different environment that may reduce the retrieval performance but HSV model gives more stability to that affect since the color information of HSV space is distributed separately from the illumination part in different channels. The performance of the image retrieval is differentiated by different wavelet transforms because they provides the image information more effectively and GLCM is an old and classic method used to describe the texture in variety of image recognition fields. GLCM is useful since it can contain three major elements, texture information, histogram information and edge information. GLCM provides the rules that gray scale of a pair of pixels appears in a certain distance away in a certain direction. Finally, a practical results show the better retrieval performance based on wavelet Comparision and glcm features.