topic regards: ◆ review of cbir ◆ line clusters for cbir ◆ npr using normal ◆ combine cbir...

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

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

[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

[Visual feature extraction]• Color– Color histogram, Color moments, Color Sets

• Texture– Co-occurrence matrix, Visual texture properties, Wavelet transform

[Visual feature extraction]• Shape– boundary-based, region-based

• Color Layout– Quadtree-based, Coherent/Incoherent

• Segmentation–Morphological operation, Computer-assisted

[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

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

Consistent Line Clusters for Building Recognition in CBIRYi Li and Linda G. Shapiro University of WashingtonPattern Recognition, 2002. Proceedings. 16th International Conference

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

Illustration of Complex Real-World Objects using Images with NormalsCorey Toler-Franklin, Adam Finkelstein and Szymon RusinkiewiczPrinceton UniversitySymposium on Non-Photorealistic Animation and Rendering 2007

[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

Intensities = Albedo * (Normal·Light Direction)

[Tools for RGBN Processing]• Gaussian Filtering– Smoothing operator

• Segmentation– RGBN segmentation is easier

• Discontinuity Lines– Adjacent pixels have very different normals

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

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

[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

[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

[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

[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)

CAT: A Techinque for Image Browing and Its Level-of-Detail ControlGomi Ai, Takayuki Itoh, Jia LiOchanomizu University The Journal of the Institute of Image Electronics Engineers of Japan (2008)

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

[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

[ 長方形の入れ子構造による階層型データ視覚化手法 ]

[ 評価実験 ]

Thank You.