基於H.264畫面內編碼特徵之影像內容檢索技術;Content Based Image Retrieval utilizing H.264 Intra Coding Features

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Communication Engineering > Electronic Thesis & Dissertation > Item 987654321/54356

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/54356

Title:	基於H.264畫面內編碼特徵之影像內容檢索技術;Content Based Image Retrieval utilizing H.264 Intra Coding Features
Authors:	楊雅婷;Yang,Ya-ting
Contributors:	通訊工程研究所
Keywords:	壓縮域;H.264;畫面內預測;內容檢索;H.264;Intra Prediction;Compression domain;Content-based Retrieval
Date:	2012-07-18
Issue Date:	2012-09-11 18:49:03 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著網際網路與手機通訊的蓬勃發展，所能取得的影音資料日漸擴大，如何在龐大的資料庫中取得使用者所需求的影像逐漸成為一個重要的課題。而以影像內容為基礎的檢索研究，雖被廣泛的研究，但仍然有缺點存在，特別是將影像全部解壓縮後再從影像擷取特徵，會耗費相當多地運算時間與儲存空間。本論文針對內容檢索，利用H.264解碼器於壓縮域上的特徵進行抽取，以畫面內預測（intra prediction）所取得之不同預測模式當為特徵，並利用其殘餘值（residual）篩選預測模式是否可靠，最後採用區域搜尋方式加上幾何對應關係進行檢索。實驗結果顯示，所提出之方法可大幅減少檢索時所需使用的大量特徵空間，且檢索效能MAP值為0.3，ANMRR值為0.625，仍能維持應有的檢索效能。With the development of internet and mobile communications, efficient multimedia retrieval from huge databases becomes an important issue. Although content-based image retrieval has been extensively studied in recent years, there still exist shortcomings. In particular, extracting features from fully decompressed image is computation and memory resource consuming. This thesis focuses on the content-based image retrieval in compression domain. The extracted features are based on the I-frame coding information in H.264 that is a powerful and widely used coding standard. We propose to employ the local mode histogram as the texture feature to match images, and apply the residual coefficients to filter non-confidence modes. Moreover, the geometrical correspondence between two images is also considered in our method. The experimental results show that the proposed method can reduce the resource consumption and archive the similar performance, i.e., MAP 0.3, and ANMRR 0.625 in Oxford 5k compared with the method that extracting features in decompressed images.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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