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Content-Based Image Retrieval

Content-Based Image Retrieval. Michele Saad Email: michele.saad@mail.utexas.edu EE-381K-14: Multi-Dimensional Digital Signal Processing March 06, 2008. Motivation. Exponential increase in computing power and electronic storage capacity

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Content-Based Image Retrieval

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  1. Content-Based Image Retrieval Michele Saad Email: michele.saad@mail.utexas.edu EE-381K-14: Multi-Dimensional Digital Signal Processing March 06, 2008

  2. Motivation • Exponential increase in computing power and electronic storage capacity • Exponential increase in digital image/video database sizes • Increased use of image and video: • Entertainment • Education • Commercial purposes • Need abstractions for efficient and effective browsing

  3. Content-Based Image Retrieval System • Feature extraction/selection • Indexing • System Design Challenge: Gap between low-level features and high level user semantics

  4. Feature Extraction • Primary Features • Color • Texture • Shape • Spatial location • Feature Selection Methods • Relevance feedback (supervised learning) • Fuzzy approach

  5. Color Features • Conventional color histogram (CCH) • Easy computation • Does not encode spatial info • Does not encode color pixel similarity • Fuzzy color histogram (FCH) • Considers degree of color similarity between pixels • Robust to quantization error • Robust to changes in light intensity • Color correlogram • Easy computation • Distills spatial correlation of colors • Color-shape based method • Includes area and shape info

  6. Color Features Key Paper #1 N. R. Howe, D. P. Huttenlocher, “Integrating Color, Texture and Geometry for Image Retrieval”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 239-246, June 2000.

  7. Texture Features Key Paper #2 • Steerable pyramid • Basic filters are translation and rotation of a single function • Filter is linear combination of basis functions • Only for rotation-invariant texture retrieval • Contourlet transform • Combination of a Laplacian pyramid and directional filter bank • Low computational complexity • Gabor wavelet • Optimally achieves joint resolution in space and spatial frequency • Computationally intensive • Highest texture retrieval results • Complex directional filter bank (CDFB) • Retrieval results comparable with Gabor wavelet results • Shift Invariant

  8. Texture Features S. Oraintara, T. T. Nguyen, “Using Phase and Magnitude Information of the Complex directional Filter Bank for Texture Image Retrieval”, Proc. IEEE Int. Conf. on Image Processing, vol. 4, pp. 61-64,Oct. 2007

  9. Texture Features S. Oraintara, T. T. Nguyen, “Using Phase and Magnitude Information of the Complex directional Filter Bank for Texture Image Retrieval”, Proc. IEEE Int. Conf. on Image Processing, vol. 4, pp. 61-64,Oct. 2007

  10. Feature Selection Key Paper #3 • Online feature selection • Relevance feedback learning • Fuzzy feature contrast model (FFCM) • Boosting algorithm • Feature contrast model (FCM) psychological similarity between two objects:

  11. Project Goal • Comparison of color, shape and texture feature extraction algorithms • Comparison of two feature selection algorithms incorporating relevance feedback. • Simulations to be done on an image dataset of 10,000 images from the misc database

  12. References • [1]. A. Bovik, Handbook of Image and Video Processing, 2nd Edition, Elsevier Academic Press, ISBN 0-12-119792-1, 2005. • [2]. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu and R. Zabih, “Time Indexing Using Color Correlograms”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 762 – 768, June 1997. • [3]. S. Oraintara and T. T. Nguyen, “Using Phase and Magnitude Information of the Complex directional Filter Bank for Texture Image Retrieval”, Proc. IEEE Int. Conf. on Image Processing, vol. 4, pp. 61-64,Oct. 2007. • [4]. W. Jiang, G. Er, Q. Dai and J. Gu, “Similarity-Based Online Feature Selection in Content-Based Image Retrieval”, IEEE Trans. on Image Processing, vol. 15, no. 3, pp. 101-104, March 2006. • [5]. M. Kokare, P.K. Biswas and B.N. Chatterji, “Texture Image Retrieval Using New Rotated Complex Wavelet Filters”, IEEE Trans. on Systems, Man and Cybernetics- Part B: Cybernetics, vol. 23, no. 6, pp. 1168 - 1178, Dec. 2005. • [6]. P Liu, K. Jia, Z. Wang and Z. Lv, “A New and Effective Image Retrieval Method Based on Combined Features”, Proc. IEEE Int. Conf. on Image and Graphics, vol. I, pp. 786-790, August 2007. • [7]. N. R. Howe and D. P. Huttenlocher, “Integrating Color, Texture and Geometry for Image Retrieval”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 239-246, June 2000. • [8]. N. V. Shirahatti and K. Barnard, “Evaluating Image Retrieval”, Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, vol. I, pp. 955-961, June 2005. • [9]. S. Deb and Y. Zhang, “An Overview of Content-Based Image Retrieval Techniques”, Proc. IEEE Int. Conf. on Advanced Information Networking and Application, vol. I, pp. 59-64, 2004.

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