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研究生:周怡君
研究生(外文):Yi-Chun Chou
論文名稱:應用於拍賣網頁分類目錄下的影像搜尋系統
論文名稱(外文):A Category-Based Image Retrieval System Applied to Online Auction
指導教授:劉震昌
指導教授(外文):Jen-Chang Liu
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:51
中文關鍵詞:搜尋系統線上圖片搜尋系統色彩直方圖區域檢索
外文關鍵詞:Color HistogramPrinciple Component AnalysisContent-Based Image RetrievalEdge
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近年來,隨著多媒體資訊的普及,數位相機、具有照像功能的手機等相關科技產品的普及化,許多研究學者開始採用影像的內容來做為圖片搜尋的依據。本篇論文主要是將此方法應用在Yahoo拍賣網頁上,並且在特定的分類目錄底下,實做一個影像搜尋系統。本搜尋系統,提供使用者上傳或點選資料庫中影像做搜尋;根據輸入的影像,首先利用了最基本的Whole Image來做特徵擷取,最後回傳拍賣網頁中最相似的圖片及拍賣網頁的網址以供使用者瀏覽。為了增進基本影像搜尋的準確率,本論文中我們提出了" Edge + PCA "的方法來取出影像中具有意義、有代表性的區域,並擷取此區域的特徵進行搜尋,回傳結果供使用者瀏覽。除此之外,我們還結合影像內容和文字來做搜尋,提供給使用者更有效率且準確的影像搜尋結果。最後,我們根據Yahoo拍賣網頁抓取的資料來做系統的評估。
Because of the advancement of multimedia technology and the popularity of digital cameras, camera-enabled mobile phone, and Internet, many researchers have recently studied to use the content of images for image retrieval. The purpose of this paper is to apply content-based image retrieval to online auction system with build-in directories. The development of an intelligent category- and content-based search engine is presented, in which it allows a user to upload an image file or choose one image from database to search. In the baseline system, we extract features from the whole image and the system returns images with similar features and also their hyperlinks to the source auction webpages for browsing. To improve the precision of image search, we propose a novel approach to extract features from the most likely object region in an image, called "Edge + PCA". The approach assumes that there is rich edge information around the object in an image. A representative region is calculated from the edge map of an image using PCA. In addition, we combine image search with text search to provide users more flexibility and examine its performance against image-only and text-only search. In the experiments, we downloaded images and related webpages from Yahoo online auction and evaluated the performance of the proposed systems.
第一章:導論
1.1 研究背景
1.2 研究動機與目的
1.3 文獻探討
1.3.1 影像搜尋系統(Content-Based Image Retrieval, 簡稱CBIR)
1.3.2 CBIR的發展
1.4 系統簡介
1.4.1 資料庫建立流程
1.4.2 系統流程
1.5 系統方法概述
1.6 論文大綱
第二章:影像特徵擷取方法與排序
2.1 基本概念
2.2 特徵擷取
2.2.1 Whole Image
2.2.2 Edge + PCA
2.2.3 Region Extraction 評估實驗
2.3 影像特徵比對與排序方法
第三章:文字切割方法與排序
3.1 英文字切割
3.2 中文字切割
3.3 文字的相似度比較與排序
第四章:結合影像與文字結果排序
第五章:系統效能評估
5.1 實驗環境與方法
5.2 實驗1:圖對圖
5.3 實驗2:圖對網頁
5.4 實驗3:文字與圖對網頁
第六章:結論與未來發展
6.1 結論
6.2 未來發展
參考文獻
Appendix
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[2] W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos and G. Taubin, "The QBIC project: Querying images by content using colour, texture and shape, " in Proceedings of the SPIE on Storage and Retrieval for Image and Video Databases, vol. 1908, pp. 173-187, 1993.
[3] http://www.hermitagemuseum.org/fcgi-bin/db2www/qbicSearch.mac/qbic?selLang=E
nglish
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[7] M. N. Do, and M. Vetterli, "Wavelet-Based Texture Retreival Using Generalized Gaussian Density and Kullback-Leibler Distance," IEEE Transactions on image processing, vol. 11, Feb, 2002.
[8] B. G. Prasad, K. K. Biswas, and S. K. Gupta, "Region-Based Image Retrieval using Integrated color, shape, and location index," Computer vision and image Understanding, vol. 94, pp. 193-233, 2004.
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[12] C. Carson, S. Belongie, H. Greenspan and J. Malik, "Blobworld: Image segmentation using Expectation-Maximization and its application to image querying," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 1026-1038, Aug, 2002.
[13] Y. A. Aslandogan and C. T. Yu, "Evaluating strategies and systems for content based indexing of person images on the web," on ACM Multimedia, pp.313-321, 2000.
[14] A. R. Smith, "Color gamut transform pairs," on ACM SIGGRAPH Computer Grapics, vol 12, pp. 12-19, 1978
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[16] M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis, and Machine Vision, 1998.
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