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.

[1]數位時代雜誌第202期special report, March, 2011.
[2]B. Girod, V. Chandrasekgar, R. Grzeszczuk, and Y. A. Reznik, “Mobile Visual Search : Architectures, Technologies, and the Emerging MPEG Standard,” IEEE Multimedia, vol. 18, no. 3, pp. 86-94, 2011.
[3]M. J. Swain, and D. H. Ballard, “Color Indexing,” International Journal of Computer Vision, pp. 11-32, 1991.
[4]D. G. Lowe, “Distinctive Image Features from Scale-InvariantKeypoints,” International Jouranl fo Computer Vision, pp. 91–110, 2004.
[5]V. Chandrasekhar, G. Takacs, D. Chen, S. Tsai, Y. Reznik, R.Grzeszczuk, and B. Girod, “Compressed Histogram of Gradients: a Lowbitrate Descriptor,” International Journal of Computer Vision, vol. 94, pp.1-16, 2011.
[6]J. R. Smith, S. -F. Chang, “Querying by color regions using the VisualSEEk content-based visual query system,” Intelligent Multimedia Information Retrieval, 1996.
[7]O. Marques, B. Furht, and T. K. Shih, “Content-based Visual Information Retrieval,” Distrivuted Multimedia Databases : Techniques and Applications, pp. 37-57, Idea Group Publishing, 2002.
[8]R. Pentland, W. Picard, and S. Sclaroff, “Photobook : Content-based Manipulation of Image Databases,” International Journal of Computer Vision, 1996.
[9]C. Carson, M. Thomas, S. Belongie, J. M. Hellerstein, and J. Malik, “Blobworld : A System for Region-based Image Indexing and Retrieval,” International Conference on Visual Information Systems, 1999.
[10]C. Faloutsos, R. Barber, M. F. J. Hafner, W. Niblack, D. Petkovic, and W. Equitz, “Efficient and Effective Querying by Image Content, ” Journal of Intelligent Information Systems, 1994.
[11]B- C. Chen, H. Jieh, “Content-based Image Retrieval of Butterflies,” 2000. http://turing.csie.ntu.edu.tw/ncnudlm/
[12]M. Ferman, A. M. Tekalp, and R. Mehrotra, "Robust Color Histogram Descriptors for Video Segment Retrieval and Identification," IEEE Transactions on Image Processing, vol. 11, no. 5, pp. 497-508, May 2002.
[13]F. Zargari, M. Mehrabi, and M. Ghanbari, "Compressed Domain Texture Based Visual Information Retrieval Method for I-Frame Coded Pictures," IEEE Transactions on Consumer Electronics, vol. 56, no. 2, pp. 728-736, May 2010.
[14]J. Feng and Y. W. Chen, "Feature Extraction Algorithm of Block Edge Pattern Based on H.264 I-Frame Coding," Journal of South China University of Technology, vol. 38, no. 2, pp. 126-131, 2010.
[15]M. Mehrabi, F. Zargari, and M. Ghanbari, "Compressed Domain Content Based Retrieval Using H.264 DC-Pictures," Multimedia Tools and Applications, Sep. 2010.
[16]X. H. Zhang, G. C. Bian, and W. B. Xu, "A Shape Feature Based Image Retrieval in DCT Compressed-Domain," in proc. Fifth International Conference on Computer and Information Technology, pp. 629-633, Sep. 2005.
[17]H. Wang, A. Divakaran, A. Vetro, S. Chang, and H. Sun, "Survey of Compressed-Domain Features Used in Audio-Visual Indexing and Analysis," Journal of Visual Communication and Image Representation, vol. 14, no. 2, pp. 150-183, Jun. 2003.
[18]Z. Droueche, M. Lamard, G. Cazuguel, G. Quellec, C. Roux, and B. Cochener, "Content-Based Medical Video Retrieval Based on Region Motion Trajectories," in proc. 5th European Conference of the International Federation for Medical and Biological Engineering, pp. 622-625, Sep. 2011.
[19]T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, “Overview of the H.264/AVC video coding standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol.13, no.7, pp.560-576, July 2003.
[20]Y. Wang, “A Improved Image Edge Detection Algorithm Based on H.264 Intra Prediction,” International Conference on Intelligence Science and Information Engineering, 2011.
[21]Oxford 5k dataset, http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/
[22]M. Zhu, “Recall, precision, and average precision,” Dept. Statistics Actuarial Sci., Univ. Waterloo, CA, Tech. Rep. 9, 2004.
[23]P. N- Nya, J. Restat, T. Meiers, J.-R. Ohm, A. Seyferth, and R. Sniehotta, “Subjective Evaluation of the MPEG-7 Retrieval Accuracy Measure (ANMRR),” ISO/WG11 MPEG Meeting, Geneva, Switzerland, Doc. M6029, May 2000.
[24]Q. Xe, J. Liu, S. Wang, and J. Zhao, “H.264/AVC baseline profile decoder optimization on independent platform,” International Conference on Wireless Communications, Networking and Mobile Computing, vol. 2, pp. 1253 – 1256, Sep. 2005.
[25]J. Phillbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, “Object Retrieval with Large Vocabularies and Fast Spatial Matching,” IEEE Conference on Computer Vision and Pattern Recognition, 2007.