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
Introduction Content-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
Introduction Target 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 。Hue distinguish colors 。Saturation the percentage of white light that is added to a pure color 。Value perceived 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 =
Quadtree Decomposition An image is divided into rectangular blocks 7x7 is the smallest block we define 7
Representative feature extraction Vector 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 extraction Representative feature extraction of objects using VQ
Representative feature extraction Representative feature extraction of objects using VQ
Implementation A Simple Follow Chart
Implementation Search Result