1. 1 Visual Information Extraction in Content-based Image Retrieval System Presented by: Mian Huang Weichuan Dong Apr 29, 2004

2. 2 Talk Outline  Introduction  The Image Retrieval System  Visual Feature Extraction  The Experiments  Discussion and Future Work

3. 3 Introduction  Motivation  Efficient Image Retrieval – large, varied digital collections Images from http://amazon.ece.utexas.edu/~qasim/samples/sample_transport.htm

4. 4 Introduction  Background  Text-based Image Retrieval  Drawback  Content-based Image Retrieval  Visual content/feature  Color – our focus  Texture  Shape  Position

5. 5 The Image Retrieval System Content-based Image Retrieval System Architecture X. Li, S. Chen, M. Shyu, B. Furht, Florida International University, Miami

6. 6 The Image Retrieval System(cont.) The role of Visual Information Extraction Image DBFeature DB Color Label Histogram Computation Segmentation Algorithm Visual Content Extraction Feature Indexing Feature Indexes here we are

7. 7 Visual Information Extraction  The Algorithm  Group - Color label histogram computation  Choosing color space - HSV  Categorizing colors - bins  Describe - Image labeling  Work on Colors – why color histogram  Robust – scaling, orientation, perspective and occlusion  Joint distribution of 3 color channels – global info.  Fast

8. 8 The Experiments  Expected results  Input  Output 1 2 12 3 4 55 11 3 2 Images from http://elib.cs.berkeley.edu/photos/blobworld/

9. 9 Future Work  Improved segmentation algorithm  Color histogram – not sufficient  Spatial information of color  Joint distribution of color, texture and spatial features  Test on images  Distinctive objects  Distinctive scenes  Distinctive objects and scenes  other