Content Based Image Retrieval by the Fusion of Short Term Learning Methods
Subject Areas : electrical and computer engineering
B.
Bagheri
1
M.
Pourmahyabadi
2
H.
Nezamabadi-pour
3
Keywords: Content based image retrieval relevance feedback semantic gap fusion short-term learning,
Abstract :
Content based image retrieval (CBIR) contains a set of techniques to process the visual features of a query image, in order to retrieve images semantically similar to it, in a database. To improve the performance of image retrieval systems, relevance feedback tool can be used. In this research, to increase the effectiveness of the image retrieval systems, the fusion of two (multiple) short term learning methods based on relevance feedback is proposed. In the proposed method, fusion is performed in three levels: fusion in ranks, fusion in retrieved images, and fusion in similarities. To evaluate the performance of the proposed method, a CBIR system with 10000 images of 82 different semantic groups is employed. The experimental results confirm the superior of suggested method in terms of retrieval precision.
[1] R. S. Torres and A. X. Falcao, "Content-based image retrieval: Theory and applications," RITA, vol. 13, no. 2, pp. 165-189, 2006.
[2] S. P. Wilson and G. Stefanou, "Bayesian approaches to content-based image retrieval," in Proc. of the Int. Workshop/Conf. on Bayesian Statistics and Its Applications, 11 pp., Varanasi, India, Jan. 2005.
[3] P. B. Patil and M. B. Kokare, "Relevance feedback in content based image retrieval: A review," J. of Applied Computer Science & Mathematics, vol. 5, no. 10, pp. 41-48, 2011.
[4] E. Rashedi, H. Nezamabadipour, and S. Saryazdi, "A Gradient Descent based Similarity Refinement Method for CBIR Systems," Tehran, Iran, 2012.
[5] X. -Y. Wang, J. -W. Chen, and H. -Y. Yang, "Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting," Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 368-381, Jan. 2012.
[6] M. E. ElAlami, "Unsupervised image retrieval framework based on rule base system," Expert Systems with Applications, vol. 38, no. 4, pp. 3539-3549, Apr.. 2011.
[7] ا. شمسی گوشكی، ح. نظام آبادی‌پور و س. سریزدی، "بررسي كارايي روش‌هاي مختلف خوشه‌بندي سلسله مراتبي در بازيابي تصوير به شيوه چند پرسشي،" مجموعه مقالات شانزدهمين كنفرانس بين‌المللي سالانه انجمن كامپيوتر ايران، صص. 373-368، تهران، 19-17 اسفند 1389.
[8] C. D. Ferreira, et al., "Relevance feedback based on genetic programming for image retrieval," Pattern Recognition Letters, vol. 32, no. 1, pp. 27-37, Jan. 2011.
[9] M. Broilo and F. G. B. De Natale, "A stochastic approach to image retrieval using relevance feedback and particle swarm optimization," IEEE Trans. on Knowledge and Data Engineering, vol. 12, no. 4, pp. 267-277, Mar. 2010.
[10] D. Liu, K. A. Hua, K. Vu, and N. Yu, "Fast query point movement techniques for large CBIR systems," IEEE Trans. on Knowledge and Data Engineering, vol. 21, no. 5, pp. 729-743, May 2009.
[11] M. Arevalillo-Herraez, F. H. Ferri, and S. Moreno-Picot, "Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval," Applied Soft Computing, vol. 11, no. 2, pp. 1782-1791, Mar. 2011.
[12] Q. Iqbal and K. Aggarwal, "Feature integration, multi-image queries and relevance feedback in image retrieval," in Proc. 6th Conf. on Visual Information Systems, pp. 467-474, Miami, FL, USA, 24-26 Sep. 2003.
[13] ا. شمسی، س. سریزدی، ح. نظام‌آبادی‌پور و م. ش. معین، "روشی جدید در بازخورد ربط برای بازیابی تصویر بر اساس محتوا به شیوه چندپرسشی،" مجله مهندسی برق دانشگاه تبریز، جلد 40، شماره 2، صص. 62-51، پاييز و زمستان 1389.
[14] O. H. Bray, "Information integration for data fusion," Strategic Business Development, Sandia National Laboratories, 1997.
[15] س. ح. نبوی کریزی و ا. کبیر، "ترکیب طبقه‌بندها: ایجاد گوناگونی و قواعد ترکیب،" مجله علوم مهندسی کامپیوتر، نشریه علمی پژوهشی انجمن کامپیوتر ایران، جلد 3، شماره 3، صص. 95-107، پاییز 1384.
[16] س. ح. نبوی کریزی و ا. کبیر، "یک روش دومرحله‌ای برای ترکیب طبقه‌بندها،" نشریه برق و مهندسی کامپیوتر ایران، سال 6، شماره 1، صص. 70-63، بهار 1387.
[17] Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra, "Relevance feedback: a power tool for interactive content-based image retrieval," IEEE Trans. Circuits Systems Video Technology, vol. 8, no. 5, pp. 644-655, Sep. 1998.
[18] S. Tong and E. Chang, "Support vector machine active learning for image retrieval," in Proc.9th Int. Conf. ACM Multimedia, pp. 107-118, 2001.
[19] P. H. Gosselin and M. Cord, "Active learning methods for interactive image retrieval," IEEE Trans. Image Processing, vol. 17, no. 7, pp. 1200-1211, Jul. 2008.
[20] J. J. Rocchio, "Relevance feedback in information retrieval," in Salton, G. (Ed.), The Smart Retrieval System: Experiments in Automatic Document Processing, Prentice Hall, pp. 313-323, 1971.
[21] Y. Rui, T. S. Huang, and S. Mehrotra, "Content-based image retrieval with relevance feedback in MARS," in Proc. IEEE Int. Conf. on Image Processing, vol. 2, pp. 815-818, Santa Barbara, CA, USA, 26-29 Oct. 1997.
[22] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, ISBN:0-21-686140-4. 1999.
[23] ح. نظام‌آبادی‌پور و ا. کبير، "ارزيابی معيارهای عدم شباهت در بازيابی و طبقه‌بندی تصوير،" مجله علمی و پژوهشی فنی مدرس، شماره ٢٢، صص. 98-89، زمستان ١٣٨4.
[24] ع. راشدي، بازيابي تصوير مبتني بر محتوا با استفاده از ترکيب يادگيري کوتاه‌مدت و يادگيري بلندمدت در فضاي معناها، رساله دکتري، دانشگاه شهيد باهنر کرمان، مرداد 1392.
[25] R. Nuray and F. Can, "Automatic ranking of information retrieval systems using data fusion," Information Processing and Management, vol. 42, no. 3, pp. 595-614, 2006.
[26] J. C. de Borda, "Memoire sur les elections au scrutin," in Histoire de l'Academie Royale des Sciences, Paris, 1970.
[27] B. Bagheri, M. Pourmahyabadi, and H. Nezamabadipour, "A novel content based image retrieval approach by fusion of short term learning methods," in Proc. 5th Conf. on Information and Knowledge Technology, pp. 355-358, Shiraz, Iran, 28-30 May 2013.
[28] G. V. Cormack, C. L. A. Clarke, and S. Buttcher, "Reciprocal rank fusion outperforms condorcet and individual rank learning methods," in Proc. of the 32nd ACM SIGIR, pp. 758-759, Boston, MA, USA, 19-23 Jul. 2009.
[29] W. Shengli and Z. Xiaoqin, "Condorcet fusion for blog opinion retrieval," in Proc. 23rd Int.l Workshop on Database and Expert Systems Applications DEXA'12, pp. 156-160, Washington, DC, USA, 3-7 Sep. 2012.
[30] M. Montague and J. Aslam, "Condorcet fusion for improved retrieval," ACM-CIKM, 2002.
[31] X. Qi, S. Barrett, and R. Chang, "A noise-resilient collaborative learning approach to content-based image retrieval," International J. of Intelligent Systems, vol. 26, no. 12, pp. 1153-1175, Dec. 2011.
[32] J. Han, K. N. Ngan, M. Li, and H. Zhang, "A memory learning framework for effective image retrieval," IEEE Trans. on Image Processing, vol. 14, no. 4, pp. 511-524, Apr. 2005.
[33] S. F. Chang, T. Sikora, and A. Puri, "Overview of the MPEG-7 standard," IEEE Trans. on Circuits and Systems for Video Technology, vol. 11, no. 6, pp. 688-695, Jun. 2001.
[34] ع. راشدی و ح. نظام‌آبادی‌پور، "یادگیری بلندمدت مبتنی بر الگوهای معنایی با بهرهگیری از اطلاعات یادگیری کوتاه‌مدت به روش بهبود تابع شباهت در سامانه‌های بازیابی تصویر،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، سال 9، شماره 4، صص. 212-203، زمستان 1390.
[35] H. Nezamabadipour and E. Kabir, "Concept learning by fuzzy k-NN classification and relevance feedback for efficient image retrieval," Expert Systems with Applications, vol. 36, no. 3, pt. 2, pp. 5948-5954, Apr. 2009.
[36] E. Rashedi, H. Nezamabadipour, and S. Saryazdi, "A simultaneous feature adaptation and feature selection method for content-based image retrieval systems," Knowledge-Based Systems, vol. 39, pp. 85-94, Feb. 2012.