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以四元樹為基礎抽取圖片物件特徵 之 影像檢索. 專題 J 組 指導教授: 曾修宜教授 組員 : 楊智宇 91156207 黃文宣 91156250 劉濬毅 91155315. TABLE OF CONTENT. Introduction Image Features 。 Color 。 Texture Quadtree Decomposition
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以四元樹為基礎抽取圖片物件特徵之影像檢索 專題J組指導教授:曾修宜教授組員 : 楊智宇 91156207 黃文宣 91156250 劉濬毅 91155315
TABLE OF CONTENT • Introduction • Image Features。Color。Texture • Quadtree Decomposition • Representative feature extraction。Vector quantization (VQ) algorithm。 Representative feature extraction of objects using VQ • Implementation
IntroductionContent-Based Image Retrieval • An application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases
IntroductionTarget • 我們想要達到的目的為:1. 讓程式透過Quadtree可以縮小人類感官與電腦處理的差距2. 增加search系列套圖的成功率3. 減低圖片處理的資料量
Image Features - Color • HSV。Huedistinguish colors。Saturationthe percentage of white light that is added to a pure color。Valueperceived light intensity • The HSV color model, which is similar to human perception, is most frequently used for retrieval.
Image Features - Color • RGB (Red Green Blue)
Image Features - Texture Angular Second Moment = Contrast = Correlation = Variance = Entropy =
7 7 Quadtree Decomposition • An image is divided into rectangular blocks • 7x7 is the smallest block we define
Representative feature extractionVector Quantization (VQ) algorithm • 主要概念: 配合前述的Quadtree,把圖片重新分割為block再把切割出來的block配合之前的Texture公式對應到 一個8-dimension vector space中的座標 再配合Vector Quantization把vector分群
Representative feature extractionRepresentative feature extraction of objects using VQ
Representative feature extractionRepresentative feature extraction of objects using VQ