Download
1 / 14
140 likes | 253 Views
This project focuses on utilizing Quadtree for extracting object features from images to enhance image retrieval efficiency. It includes representative feature extraction and Vector Quantization (VQ) algorithm. The aim is to bridge the gap between human perception and computer processing and improve search success rates.
E N D
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
