Abstrato

An Improved CBIR Method Using Color and Texture Properties with Relevance Feedback

MS. R. Janani, Sebhakumar.P

Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Content-Based Image Retrieval (CBIR) is the application of computer vision techniques and it gives solution to the image retrieval problem such as searching digital images in large databases. Retrieving most relevant images from a bulk database is difficult. Images can be retrieved using both color and texture based on relevance feedback technique. Image is first subjected to separation of RGB components and then the features are extracted for all the three components. Discrete Wavelet Transform (DWT) is used for analysis of textures recorded with different resolution. Histogram of Oriented Gradient (HOG) is used for extracting features for the images. CORAL image Database is used to find the relevant images using MATLAB Software. Feedback from the user helps to identify the images accurately even if the color and texture property method fails to attain the accuracy. Similarity comparison technique is used for good performance of the CBIR system. The proposed system is operated on a JPEG image database containing 1000 general-use color images. Helps to improve the accuracy and reduces time consumption in filtering same images.