Image Similarity Matching Based on Colour and Texture

论文摘要

Retrieving images from large and varied collections using content as a key is a challenging and important problem. The purpose of this paper is to find out the solution to the problem of designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes as well as its vast deployment in various applications, the need for CBIR development has increased. This paper outlines a description of the primitive features of an image such as colour and texture. These features are extracted and used as the basis for a similarity check between images. This paper describes how to extract the primitive features of a query image and compare them to those of database images. The first step in the process is extracting image features to a distinguishable extent. And the second step involves matching these features to yield a result that is visually similar.To extract image using colour feature I propose colour histogram method. First, we supply a query image and we compare histogram quadratic distances

论文目录

• LIST OF TABLES:
• LIST OF FIGURES:
• ABSTRACT
• ACKNOWLEDGEMENTS
• 1.INTRODUCTION:
• 2.RELATED WORKS:
• 2.1.IBM QBIC:
• 2.2.VIRAGE:
• 2.3.PHOTOBOOK:
• 2.4.VISUALSEEK:
• 2.5.IMatch:
• 3.ALGORITHM:
• 3.1.Texture:
• 3.1.1.Energy Level Algorithm:
• 3.1.2.Euclidean Distance Algorithm:
• 3.2.Colour:
• 3.2.1.Colour Histograms:
• 3.2.2.The Global Color Histogram:
• 3.2.3.The Local Color Histogram:
• 3.2.4.Similarity matrix:
• 3.2.5.Average Weight Algorithm:
• 3.2.6.Score or Distance:
• 4.EXPERIMENTAL RESULT
• 5.GUI
• 6.CONCLUSIONS
• 7.REFERENCES
• 8.APPENDIX: MATLAB CODE
• 8.1.SearchImage．m
• 8.2.MakeKernel．m
• 8.3.GFCREATEFILTER．M
• 8.4.GFCHECKFILTER．M
• 8.5.OBTAINENERGIES．M
• 8.6.DECOMPOSEIMAGE．M
• 8.7.ENERGYLEVEL．M
• 8.8.EUCLIDEANDISTANCE．M
• 8.9.QUADRATICDISTANCE．M
• 8.10.SIMILARITYMATRIX．M
• PUBLISHED PAPER