Tra cứu ảnh theo nội dung sử dụng tập Pareto và mô hình học thống kê CART

  • Vũ Văn Hiệu
  • Nguyễn Trường Thắng
  • Nguyễn Hữu Quỳnh
  • Ngô Quốc Tạo


Image retrieval systems adopt a combination of multiple features and then total distance measures of particular features for ranking the results. Therefore, the top-ranked images with smallest total distance measures are returned to the users. However, images with smallest partial distance measures which are suitable for users’ purpose may not be included in these results. Therefore, partial distance measure should be considered. In this paper, we propose to adopt the Pareto set in the distance measure space. This set assures that the returned results contain not only points with smallest total distance obtained by linear combinations, but also other points have smallest partial distance measures which cannot be found by the linear combination in the distance measure space. Especially, the searching space based on the distance measures is compacted by our algorithm, namely PDFA. This algorithm collects all the Pareto set with different depths, and is efficient for the classification and regression tree (CART). The experimental results on three image collections show the effectiveness of our proposed method.


AREVALILLO-HERRÁEZ, MIGUEL, FRANCESC J. FERRI, and SALVADOR MORENO-PICOT, Improving distance based image retrieval using non-dominated sorting genetic algorithm, Pattern Recognition Letters 53 (2015): 109-117.

BAI, CONG, KIDIYO KPALMA, and JOSEPH RONSIN, Color textured image retrieval by combining texture and color features, Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European. IEEE, 2012.

BIGGS, DAVID, BARRY DE VILLE, and ED SUEN, A method of choosing multiway partitions for classification and decision trees, Journal of Applied Statistics18.1 (1991): 49-62.

BREIMAN, LEO, et al, Classification and regression trees, CRC press, 1984.

ŞAYKOL, EDIZ, UĞUR GÜDÜKBAY, and ÖZGÜR ULUSOY, A histogram-based approach for object-based query-by-shape-and-color in image and video databases, Image and Vision Computing 23.13 (2005): 1170-1180.

DATTA, RITENDRA, et al, Image retrieval: Ideas, influences, and trends of the new age, ACM Computing Surveys (CSUR) 40.2 (2008): 5.

DENG, YINING, et al, An efficient color representation for image retrieval, Image Processing, IEEE Transactions on 10.1 (2001): 140-147.

DOS SANTOS, J. A., et al, A relevance feedback method based on genetic programming for classification of remote sensing images, Information Sciences 181.13 (2011): 2671-2684.

DUBEY, RAJSHREE S., RAJNISH CHOUBEY, and JOY BHATTACHARJEE, Multi feature content based image retrieval, International Journal on Computer Science and Engineering 2.6 (2010): 2145-2149.

FEI-FEI, LI, ROB FERGUS, and PIETRO PERONA, Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories, Computer Vision and Image Understanding106.1 (2007): 59-70.

FERREIRA, CRISTIANO D., et al, Relevance feedback based on genetic programming for image retrieval, Pattern Recognition Letters 32.1 (2011): 27-37.

HSIAO, KO-JEN, JEFF CALDER, and ALFRED O. HERO, Pareto-Depth for Multiple-Query Image Retrieval, Image Processing, IEEE Transactions on 24.2 (2015): 583-594.

HUANG, JING, et al. Image indexing using color correlograms. Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. IEEE, 1997.

JIANG, WEI, GUIHUA ER, and QIONGHAI DAI, Boost SVM active learning for content-based image retrieval, Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on. Vol. 2. IEEE, 2003.

KNOWLES, JOSHUA D., and David W. Corne, Approximating the nondominated front using the Pareto archived evolution strategy, Evolutionary computation8.2 (2000): 149-172.

KORYTKOWSKI, MARCIN, LESZEK RUTKOWSKI, and RAFAŁ SCHERER, Fast image classification by boosting fuzzy classifiers, Information Sciences 327 (2016): 175-182.

LI, JIA, and JAMES Z. WANG, Automatic linguistic indexing of pictures by a statistical modeling approach, Pattern Analysis and Machine Intelligence, IEEE Transactions on 25.9 (2003): 1075-1088.

MACARTHUR, SEAN D., CARLA E. BRODLEY, and CHI-REN SHYU, Relevance feedback decision trees in content-based image retrieval, Content-based Access of Image and Video Libraries, 2000, Proceedings, IEEE Workshop on, IEEE, 2000.

MÜLLER, HENNING, et al, Performance evaluation in content-based image retrieval: overview and proposals, Pattern Recognition Letters 22.5 (2001): 593-601.

OLIVA, AUDE, and ANTONIO TORRALBA, Modeling the shape of the scene: A holistic representation of the spatial envelope, International journal of computer vision42.3 (2001): 145-175.

PHILBIN, JAMES, et al, Object retrieval with large vocabularies and fast spatial matching, Computer Vision and Pattern Recognition, 2007, CVPR'07, IEEE Conference on, IEEE, 2007.

RAHMAN, M. M., BIPIN C. DESAI, and PRABIR BHATTACHARYA, Multi–modal interactive approach to ImageCLEF 2007 photographic and medical retrieval tasks by CINDI, Working Notes of CLEF 7 (2007).

RUI, YONG, et al, Automatic matching tool selection using relevance feedback in MARS, Proc. of 2nd Int. Conf. on Visual Information Systems, 1997.

RUI, YONG, THOMAS S. HUANG, and SHARAD MEHROTRA, Content-based image retrieval with relevance feedback in MARS, Image Processing, 1997 Proceedings., International Conference on, Vol. 2, IEEE, 1997.

RUI, YONG, et al, Relevance feedback: a power tool for interactive content-based image retrieval, Circuits and Systems for Video Technology, IEEE Transactions on 8.5 (1998): 644-655.

RUI, YONG, THOMAS S. HUANG, and SHIH-FU CHANG, Image retrieval: Current techniques, promising directions, and open issues, Journal of visual communication and image representation 10.1 (1999): 39-62.

SALTON, GERARD, and MICHAEL J. MCGILL, Introduction to modern information retrieval, (1986).

Swain, Michael J., and Dana H. Ballard, Color indexing, International journal of computer vision 7.1 (1991): 11-32.

TIEU, KINH, and PAUL VIOLA, Boosting image retrieval, International Journal of Computer Vision 56.1-2 (2004): 17-36.

TONG, SIMON, and EDWARD CHANG, Support vector machine active learning for image retrieval, Proceedings of the ninth ACM international conference on Multimedia, ACM, 2001.

TORLONE, RICCARDO, PAOLO CIACCIA, and U. ROMATRE, Which are my preferred items, Workshop on Recommendation and Personalization in E-Commerce, 2002.

TORRES, RICARDO DA S., et al, A genetic programming framework for content-based image retrieval, Pattern Recognition 42.2 (2009): 283-292.

Yu, Hui, et al, Color texture moments for content-based image retrieval, Image Processing. 2002. Proceedings. 2002 International Conference on. Vol. 3. IEEE, 2002.

YU, JIE, et al, Integrating relevance feedback in boosting for content-based image retrieval, Acoustics, Speech and Signal Processing, 2007, ICASSP 2007, IEEE International Conference on. Vol. 1, IEEE, 2007.

ZHANG, DENGSHENG, et al, Content-based image retrieval using Gabor texture features, IEEE Pacific-Rim Conference on Multimedia, University of Sydney, Australia. 2000.

ZHANG, QIANNI, and EBROUL IZQUIERDO, Optimizing metrics combining low-level visual descriptors for image annotation and retrieval, Acoustics, Speech and Signal Processing, 2006, ICASSP 2006 Proceedings, 2006 IEEE International Conference on, Vol, 2. IEEE, 2006.

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