Topic regards: ◆ Review of CBIR ◆ Line clusters for CBIR

1. Topic regards: ◆Review of CBIR ◆Line clusters for CBIR ◆NPR using normal ◆Combine CBIR & NPR ◆ Search result visualization Yuan-Hao Lai

2. Image Retrieval: Current Techniques, Promising Directions, and Open Issues Yong Rui, Thomas S. Huang University of Illinois at Urbana-Champaign Journal of Visual Communication and Image Representation 10, 39–62 (1999)

5. [Fundamental bases for CBIR] • Visual feature extraction • Basis of CBIR, No single best presentation • Multidimensional indexing • High dimensionality, Non-Euclidean similarity • Retrieval system design • CBIR system been built

6. [Visual feature extraction] • Color • Color histogram, Color moments, Color Sets • Texture • Co-occurrence matrix, Visual texture properties, Wavelet transform

7. [Visual feature extraction] • Shape • boundary-based, region-based • Color Layout • Quadtree-based, Coherent/Incoherent • Segmentation • Morphological operation, Computer-assisted

8. [Multidimensional indexing] • Dimension Reduction • Karhuan-Loeve, Clustering • Multidimensional Indexing Techniques • k-d tree, quad-tree, K-D-B tree, hB-tree, R-tree, Neural nets

9. [Retrieval system design] • random browsing • search by example • search by sketch • search by text (keyword) • navigation with customized image categories

11. Consistent Line Clusters for Building Recognition in CBIR Yi Li and Linda G. Shapiro University of Washington Pattern Recognition, 2002. Proceedings. 16th International Conference

12. [Consistent Line Clusters] • Inter/Intra-relationships among clusters • Mid-level feature • Useful in recognizing and searching man-made objects

18. Illustration of Complex Real-World Objects using Images with Normals Corey Toler-Franklin, Adam Finkelstein and Szymon Rusinkiewicz Princeton University Symposium on Non-Photorealistic Animation and Rendering 2007

21. [Non-Photometric Rendering] • From a 2D image • Too difficult to render • Using 3D Models • Too expensive to scan model • Images with Normals (RGBN) • Easy to acquire

22. Intensities = Albedo * (Normal·Light Direction)

23. [Tools for RGBN Processing] • Gaussian Filtering • Smoothing operator • Segmentation • RGBN segmentation is easier • Discontinuity Lines • Adjacent pixels have very different normals

25. [Limitations] • Dark, shiny, translucent, intereflecting objects is not suitable • Normals may also be noisy • Difficult to change the view

26. Non-Photorealistic Rendering and Content-Based Image Retrieval Xiaowen Ji, Zoltan Kato, and Zhiyong Huang National University of Singapore, Singapore Pacific Graphics (2003)

27. [Problems of CBIR] • Which low-level features is the best to measure the similarity of images • Color is important in human perception but histogram cannot provide spatial distribution of colors

28. [How do humans interpret an image] • A talented painter will give a painted interpretation of the world • Plain surfaces paint with greater strokes • Provides information about both color and structural properties

31. [The CBIR Method] • Strokes is sorted by size during rendering • Match color, orientation, position of each stroke by order • Compute the Similarity Value • Segmentation & Semantic Measurement

32. [The CBIR Method] • More index time and use more CPU • Can be done offline • More closer to human perception • Indexing can be done on small thumbnails (with smaller brushes)

34. CAT: A Techinque for Image Browing and Its Level-of-Detail Control Gomi Ai, Takayuki Itoh, Jia Li Ochanomizu University The Journal of the Institute of Image Electronics Engineers of Japan (2008)

35. CAT: 大量画像の一覧可視化と詳細度制御の一手法 五味愛, 伊藤貴之, Jai Li お茶の水女子大学大学院 画像電子学会誌37(4), 436-443, 2008-07-25

37. [Clustered Album Thumbnails] • 一覧表示と詳細度制御の画像クラスタリング • ボトムアップ形式の木構造グラフ • 対話的操作と連動インタフェース • 平安京ビュー

38. [長方形の入れ子構造による階層型データ視覚化手法][長方形の入れ子構造による階層型データ視覚化手法]

39. [評価実験]

40. Thank You.