topic regards: ◆ review of cbir ◆ line clusters for cbir
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Topic regards: ◆ Review of CBIR ◆ Line clusters for CBIR ◆ NPR using normal ◆ Combine CBIR & NPR ◆ Search result visualization. Yuan-Hao Lai. Image Retrieval: Current Techniques, Promising Directions, and Open Issues. Yong Rui, Thomas S. Huang - PowerPoint PPT PresentationTRANSCRIPT
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Topic regards:◆ Review of CBIR ◆Line clusters for CBIR◆ NPR using normal ◆ Combine CBIR & NPR ◆ Search result visualizationYuan-Hao Lai
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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)
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[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
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[Visual feature extraction]• Color– Color histogram, Color moments, Color Sets
• Texture– Co-occurrence matrix, Visual texture properties, Wavelet transform
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[Visual feature extraction]• Shape– boundary-based, region-based
• Color Layout– Quadtree-based, Coherent/Incoherent
• Segmentation–Morphological operation, Computer-assisted
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[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
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[Retrieval system design]• random browsing• search by example• search by sketch• search by text (keyword)• navigation with customized image categories
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Consistent Line Clusters for Building Recognition in CBIRYi Li and Linda G. Shapiro University of WashingtonPattern Recognition, 2002. Proceedings. 16th International Conference
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[Consistent Line Clusters]• Inter/Intra-relationships among clusters• Mid-level feature• Useful in recognizing and searching man-made objects
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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
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[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
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Intensities = Albedo * (Normal·Light Direction)
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[Tools for RGBN Processing]• Gaussian Filtering– Smoothing operator
• Segmentation– RGBN segmentation is easier
• Discontinuity Lines– Adjacent pixels have very different normals
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[Limitations]• Dark, shiny, translucent, intereflecting objects is not suitable• Normals may also be noisy• Difficult to change the view
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Non-Photorealistic Rendering and Content-Based Image RetrievalXiaowen Ji, Zoltan Kato, and Zhiyong Huang National University of Singapore, Singapore Pacific Graphics (2003)
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[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
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[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
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[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
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[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)
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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)
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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
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[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
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[ 長方形の入れ子構造による階層型データ視覚化手法 ]
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[ 評価実験 ]
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Thank You.