Một phương pháp mới chuẩn hoá dữ liệu và hiệu chỉnh trọng số cho tổ hợp đặc trưng trong tra cứu ảnh theo nội dung
AbstractRelevance feedback as a bridge between high level semantic concepts and low features. It is important to improve the performance of content based image retrieval (CBIR) is preprocessing image features and refining distance measures for query based on user information needs. We propose a novel method to normilize features and distance for CBIR using combination features. In addition, we also use relevant feedback from users and learning from low features to update weights distance measures and refine query. Experimental results over the benchmark Corel dataset demonstrate the effectiveness of this propose method.
ANDROUTSOS, PANAGIOTIS, et al, Aggregation of color and shape features for hybrid query generation in content based visual information retrieval, Signal Processing 85.2 (2005): 385-393.
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.
BEZDEK, JAMES C, Pattern recognition with fuzzy objective function algorithms, Springer Science & Business Media, 2013.
BHANU, BIR, AND ANLEI DONG, Concepts learning with fuzzy clustering and relevance feedback, Engineering Applications of Artificial Intelligence 15.2 (2002): 123-138.
BINAGHI, E., et al, Fuzzy reasoning approach to similarity evaluation in image analysis, International Journal of Intelligent Systems 8.7 (1993): 749-769.
CHANG, SHIH-FU, et al, Visual information retrieval from large distributed online repositories, Communications of the ACM 40.12 (1997): 63-71.
CIOCCA, GIANLUIGI, and RAIMONDO SCHETTINI, A relevance feedback mechanism for content-based image retrieval, Information processing & management 35.5 (1999): 605-632.
COX, INGEMAR J., et al. "The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments." Image Processing, IEEE Transactions on 9.1 (2000): 20-37.
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.
DENG, YINING, et al, An efficient color representation for image retrieval, Image Processing, IEEE Transactions on 10.1 (2001): 140-147.
DO, MINH N., and MARTIN VETTERLI, Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance, Image Processing, IEEE Transactions on 11.2 (2002): 146-158.
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.
GRIGOROVA, ANELia, et al, Content-based image retrieval by feature adaptation and relevance feedback, Multimedia, IEEE Transactions on 9.6 (2007): 1183-1192.
Gudivada, Venkat N., and Vijay V. Raghavan, Content based image retrieval systems, Computer 28.9 (1995): 18-22.
HARALICK, ROBERT M., KARTHIKEYAN SHANMUGAM, and ITS' HAK DINSTEIN, Textural features for image classification, Systems, Man and Cybernetics, IEEE Transactions on 6 (1973): 610-621.
Harman, Donna, Relevance Feedback and Other Query Modification Techniques, (1992): 241-263.
HIEU VU VAN, QUYNH NGUYEN HUU, and HA NGUYEN THI THU, Content based image retrieval with bin of color histogram, Audio, Language and Image Processing (ICALIP), 2012 International Conference on, IEEE, 2012.
HIREMATH, P. S., S. SHIVASHANKAR, and JAGADEESH PUJARI, Wavelet based features for color texture classification with application to CBIR, International Journal of Computer Science and Network Security 6.9A (2006): 124-133.
HUANG, JING, S. RAVI KUMAR, and MANDAR MITRA, Combining supervised learning with color correlograms for content-based image retrieval, Proceedings of the fifth ACM international conference on Multimedia, ACM, 1997.
HUANG, JING, et al, Image indexing using color correlograms, Computer Vision and Pattern Recognition, 1997, Proceedings., 1997 IEEE Computer Society Conference on, IEEE, 1997.
HUANG, ZHI-CHUN, et al, Content-based image retrieval using color moment and Gabor texture feature, Machine Learning and Cybernetics (ICMLC), 2010 International Conference on, Vol. 2, IEEE, 2010.
ISHIKAWA, YOSHIHARU, RAVISHANKAR SUBRAMANYA, and CHRISTOS FALOUTSOS, MindReader: Querying databases through multiple examples, Computer Science Department (1998): 551.
JOSE, SEBIN, and PHILUMON JOSEPH, Content based Image Retrieval System with Watermarks and Relevance Feedback, International Journal of Computer Applications 99.11 (2014): 1-6.
KIM, DEOK-HWAN, CHIN-WAN CHUNG, and KOBUS BARNARD, Relevance feedback using adaptive clustering for image similarity retrieval, Journal of Systems and Software 78.1 (2005): 9-23.
KOKARE, MANESH, PRABIR K. BISWAS, and BISWANATH N. CHATTERJI, Texture image retrieval using new rotated complex wavelet filters, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 35.6 (2005): 1168-1178.
LU, YE, et al, A unified framework for semantics and feature based relevance feedback in image retrieval systems, Proceedings of the eighth ACM international conference on Multimedia, ACM, 2000.
MANJUNATH, BANGALORE S., and WEI-YING MA, Texture features for browsing and retrieval of image data, Pattern Analysis and Machine Intelligence, IEEE Transactions on 18.8 (1996): 837-842.
MEHROTRA, SHARAD, et al, Multimedia analysis and retrieval system, Proc. of The 3rd Int, Workshop on Information Retrieval Systems, 1997.
MEHTRE, BABU M., MOHAN S. KANKANHALLI, and WING FOON LEE, Content-based image retrieval using a composite color-shape approach, Information Processing & Management 34.1 (1998): 109-120.
MEILHAC, CHRISTOPHE, and CHAHAB NASTAR, Relevance feedback and category search in image databases, Multimedia Computing and Systems, 1999, IEEE International Conference on, Vol. 1, IEEE, 1999.
MINKA, THOMAS P., and ROSALIND W. PICARD, Interactive learning with a “society of models”, Computer Vision and Pattern Recognition, 1996, Proceedings CVPR'96, 1996 IEEE Computer Society Conference on, IEEE, 1996.
MOGHADDAM, H. ABRISHAMI, and M. SAADATMAND-TARZJAN, Gabor wavelet correlogram algorithm for image indexing and retrieval, Pattern Recognition, 2006, ICPR 2006, 18th International Conference on, Vol. 2, IEEE, 2006.
OHANIAN, PHILIPPE P., and RICHARD C. DUBES, Performance evaluation for four classes of textural features, Pattern recognition 25.8 (1992): 819-833.
OLIVA, AUDE, and ANTONIO TORRALBA, Modeling the shape of the scene: A holistic representation of the spatial envelope, International journal of computer vision 42.3 (2001): 145-175.
ORTEGA, MICHAEL, et al, Supporting similarity queries in MARS, Proceedings of the fifth ACM international conference on Multimedia, ACM, 1997.
PASS, GREG, RAMIN ZABIH, and JUSTIN MILLER, Comparing images using color coherence vectors, Proceedings of the fourth ACM international conference on Multimedia, ACM, 1997.
PENTLAND, ALEX, ROSALIND W. PICARD, and STAN SCLAROFF, Photobook: Content-based manipulation of image databases, International journal of computer vision 18.3 (1996): 233-254.
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).
RAVANI, REZA, MOHAMAD REZA MIRALI, and MARYAM BANIASADI, Parallel CBIR system based on color coherence vector, 17th International Conference on Systems, Signals and Image Processing, 2010.
ROCCHIO, JJ, Relevance Feedback in Information Retrieval, SMART Retrieval System Experimens in Automatic Document Processing (1971).
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).
SCLAROFF, STAN, LEONID TAYCHER, and MARCO LA CASCIA, Imagerover: A content-based image browser for the world wide web, Content-Based Access of Image and Video Libraries, 1997, Proceedings, IEEE Workshop on, IEEE, 1997.
SMITH, JOHN R., and SHIH-FU CHANG, VisualSEEk: a fully automated content-based image query system, Proceedings of the fourth ACM international conference on Multimedia, ACM, 1997.
SPINK, AMANDA, and ROBERT M. LOSEE, Feedback in information retrieval, Annual review of information science and technology 31 (1996): 33-78.
STRICKER, MARKUS A., and MARKUS ORENGO, Similarity of color images, IS&T/SPIE's Symposium on Electronic Imaging: Science & Technology. International Society for Optics and Photonics, 1995.
`SU, ZHONG, et al, Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning, Image Processing, IEEE Transactions on 12.8 (2003): 924-937.
SWAIN, MICHAEL J., and DANA H. BALLARD, Color indexin, International journal of computer vision 7.1 (1991): 11-32.
TAMURA, HIDEYUKI, SHUNJI MORI, and TAKASHI YAMAWAKI, Textural features corresponding to visual perception, Systems, Man and Cybernetics, IEEE Transactions on 8.6 (1978): 460-473.
WANG, TAO, YONG RUI, and SHI-MIN HU, Optimal adaptive learning for image retrieva, Computer Vision and Pattern Recognition, 2001, CVPR 2001, Proceedings of the 2001 IEEE Computer Society Conference on, Vol. 1. IEEE, 2001.
YANG, MIIN-SHEN, PEI-YUAN HWANG, and DE-HUA CHEN, Fuzzy clustering algorithms for mixed feature variables, Fuzzy Sets and Systems 141.2 (2004): 301-317.
ZHANG, DENGSHENG, et al, Content-based image retrieval using Gabor texture features, IEEE Pacific-Rim Conference on Multimedia, University of Sydney, Australia, 2000.