mmsp08_slides - Content Based Image Retrieval Using...

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www.infotech.monash.edu Content Based Image Retrieval Content Based Image Retrieval Using Using Curvelet Curvelet Transform Transform Ishrat Ishrat Jahan Jahan Sumana Sumana , , Md Md . . Monirul Monirul Islam, Islam, Dengsheng Zhang and Dengsheng Zhang and Guojun Guojun Lu Lu Gippsland School of Info Tech, Monash University, Gippsland School of Info Tech, Monash University, Churchill, Victoria, 3842 Churchill, Victoria, 3842 {dengsheng.zhang dengsheng.zhang , , [email protected] [email protected]
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www.infotech.monash.edu 2 Outline Outline Motivation Motivation Texture Features Texture Features Curvelet Curvelet Transform Transform Experiments and Results Experiments and Results Conclusions and Outlook Conclusions and Outlook
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www.infotech.monash.edu 3 Content Based Image Retrieval Content Based Image Retrieval •How to organize and find images •Search images based on content
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www.infotech.monash.edu 4 Texture Features Texture Features What are they? What are they? Higher level • Contrast • Directionality • Regularity • Smoothness Low level • Edges • Repeating patterns • Variation frequency Higher level texture features are difficult to define and obtain
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www.infotech.monash.edu 5 Texture Features Texture Features Approaches Approaches Spatial Features—sensitive to noise – Statistical: mean, variance, skewness, kurtosis, co- occurrence matrix, moments – Probabilistic: Markov models – Structural:, regularity, directionality, roughness – Histogram: edge – Fractal: box counting Spectral Features—robust, so far the best – Wavelet – Gabor Filters –Cu rve le t
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www.infotech.monash.edu
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This note was uploaded on 05/22/2011 for the course COMP 207 taught by Professor Zhangli during the Spring '11 term at University of Liverpool.

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mmsp08_slides - Content Based Image Retrieval Using...

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