Content-based image retrieval using shape and texture attributes

TAMPERE UNIVERSITY OF TECHNOLOGY Degree Program in Electrical Engineering Institute of Signal Processing Partio, Mari: Content-based Image Retrieval using Shape and Texture Attributes Master of Science Thesis, p. 70 Examiners: Professor Moncef Gabbouj, Researcher Bogdan Cramariuc Funding: Center of Excellence, SPAG, Academy of Finland Department of Electrical Engineering November 2002 Due to rapid increase in volume of image and video collections, traditional methods of indexing and retrieval using only keywords have become outdated. Therefore, alternative methods to describe images using their visual content have been developed. To produce and test algorithms for content-based image and video retrieval, MUVIS (Multimedia Video Indexing and Retrieval System) was developed at TUT. The goal of MUVIS is a fast, real-time and reliable audio/video (AV) browsing and indexing application, which is also capable of extracting some key features (such as color, texture and shape) of the AV media. Most of the existing image retrieval systems perform reasonably when using color features. However, retrieval accuracy using shape or texture features does not produce as good results. Therefore, this thesis investigates different methods of representing shape and texture in content-based image retrieval. Later, when appropriate segmentation algorithms are available some of these methods could also be applied to video object retrieval. The thesis presents two contributions: one is shape-based and the second is texture-based retrieval method. The former contribution concerns shape analysis and retrieval. Shape attributes can be roughly divided into two main categories: boundary-based and regionbased. Since the human visual system itself focuses on edges and ignores uniform regions, this thesis concentrates on boundary-based representations. A novel boundary-based method using distance transformation and ordinal correlation is developed in this thesis. Simulation results show that the proposed technique produced encouraging results when using MPEG-7 shape test database. The second contribution of the thesis is a constrained application in which the database contains a set of rock images. In this application, we applied a technique based on GrayLevel Co-occurrence matrices (GLCM) and compared the results with a well-known method from the literature. It was found that GLCM outperforms Gabor Wavelet features when considering retrieval time and visual quality of the results.