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A Hybrid Approach to Content Based Image Retrieval Using Computational Intelligence Techniques
Objectives: Improvement in the retrieval performance of the system can be brought by fusion of image features with different similarity measures. Performance of the system can be further improved by incorporating user's feedback into the system. Method: An extensive system is proposed based on one of the computational intelligence techniques for the effective retrieval of required images from systems. The proposed technique consists of two main modules, namely: feature vector processing and result enhancement. Feature fusion is performed by application of genetic operators to find the final distance between probe and stored images. Subsequently estimated retrieved images are presented to the user, first k-images will be selected by the user as K-NN query images and are ranked according to their relevance values provided by the user. Optimization of retrieved images is done by iteratively providing user's feedback to the system. Findings: Feature fusion and effective relevance feedback methods can contribute extensive benefits to content based image retrieval. Feature fusion combines different image features in such a way to get a single feature vector for all of them but combination of different image features however is not always beneficial. Semantic gap is reduced by providing user's feedback after the retrieval of images. This is an iterative process and it defines a set of relevant images in the end. Genetic algorithm based search is applied for a series of weighting functions which helps to maximize the fitness function. Applications/Improvements: Multifarious features are introduced where different image features are combined with different similarity measures and the final distance is drawn by employing genetic programming technique.
Feature Extraction, Image Descriptor, Multifarious Features, Relevance Feedback, Similarity Measure.
- Felci Rajam I, Valli S. A survey on content based image retrieval. Life Science Jounal. 2013; 10(2):1-13.
- Chen CC, Chu H. Similarity measurement between images. In Proceedings of 29th Annual International Computer Softwareand Applications Conference, Compasc’05. 2005 July; p. 41-42.
- Lakdashti A, Shahram Moin M, Badie K. Semantic-based image retrieval: A fuzzy modeling approach. IEEE/ACS International Conference on Computer Systems and Applications. AICCSA’08. 2008 Apr; p. 575-81.
- Smeulders AWM, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. IEEE Transactionson Pattern Analysis and Machine Intelligence. 2000 Dec; 22(12) p. 1349–80.
- Bugatti PH, Traina AJM, Traina C. Improving contentbased retrieval of medical images through dynamic distance on relevance feedback. 24th International Symposium on Computer-Based Medical Systems (CBMS). 2011 Jun; p. 1–6.
- De Amo S, Mendonca MG, dos Santos JA, Ferreira CD, Torres RDS, Goncalves MA, Lamparelli RAC. A relevance feedback method based on genetic programming for classification of remote sensing images. Information Sciences (Ny). 2011 Jul; 181(13):2671–84.
- Avalhais LPS, Da Silva SF, Rodrigues JF, Traina AJM, Traina C. Feature Space Optimization for Content-Based Image Retrieval. Applied Computing Review. 2012 Sep; 12(3):7– 19.
- Struc V, Pavesic N. A case study on appearance based feature extraction techniques and their susceptibility to image degradations for the task of face recognition. World Academy of Science, Engineering and Technology. 2009 Jun; 3(6):811-19.
- Torres RS, Falcao XA, Goncalves MA, Zhang B, Fan W, Fox EA, Calado PP. A Framework to Combine Descriptors for Content-based Image Retrieval. In Proc. of 14th ACM Conference on Information and Knowledge Management. 2005; p. 335–36.
- Zhou XS, Huang TS. Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems. 2003 Apr; 8(6):536-44.
- Giacinto G, Rolli F. Instance-based relevance feedback for image retrieval. Advances in Neural Information. 2004; p. 1-4.
- Setia L, Ick J, Burkhardt H. SVM-based relevance feedback in image retrieval using invariant feature histograms. IAPR Workshop on Machine Vision Applications (MVA). 2005 May; p. 1-4.
- Wu RS, Chung WH. Ensemble one-class support vector machines for content-based image retrieval. Expert Systems with Applications. 2009 Apr; 36(3):4451-59.
- Stejic Z, Takama Y, Hirota K. Genetic algorithms for a family of image similarity models incorporated in the relevance feedback mechanism. Applied Soft Computing. 2003 Jan; 2(4):306-27.
- Torres RDS, Falcao AX, Goncalves MA, Papa JP, Zhang B, Fan W, Fox E. A genetic programming framework for contentbased image retrieval. Pattern Recognition. 2009 Feb; 42(2):283-92.
- Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R. Image indexing using color correlogram. IEEE International Conference on Computer Vision and Pattern Recognition. 1997 Jun; p. 1-7.
- Torres S, Falcao AX. Content-Based Image Retrieval : Theory and Applications RITA. 2006.
- Chandy DA, Johnson JS, Selvan SE. Texture feature extraction using gray level statistical matrix for content-based mammogram retrieval. Multimedia Tools Applications. 2014 Sep; 72(2):2011-24.
- Deselaers T, Weyand T, Keysers D, Macherey W, Ney H. Berlin Heidelberg: Springer-Verlag: FIRE in ImageCLEF 2005 : Combining Content-based Image Retrieval with Textual Information Retrieval. Accessing Multilingual Information Repositories. 2006 Nov; p. 652-61.
- Lam M, Disney T, Pham M, Raicu D, Furst J, Susomboon R. Content-Based Image Retrieval for Pulmonary Computed Tomography Nodule Images. Proc. SPIE 6516, Medical Imaging. 2007 Mar.
- Fallouts C, Barber R, Flickner M, Hafner J, Niblack W, Petkovic D, Equitz W. Efficient and effective querying by image content. Journal of Intelligent Information Systems. 1994 Jul; 3(3):231-26.
- Zhang G, MaZ M, Tong Q, He Y, Zhao T. Shape Feature Extraction Using Fourier Descriptors with Brightness in Content-based Medical Image Retrieval. IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing. 2008 Aug; p. 71-74.
- Kauppinen H, Seppanen T, Pietikainen M. An experimental comparison of autoregressive and Fourier based descriptors in 2D shape classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1995; p. 201–7.
- Vijay J, Kanagaraj Bommanna Raja. Performance Evaluation of Image Retrieval System Based on Error Metrics. Indian Journal of Science and Technology. 2015; 8(7):117-22.
- Vijay J, Bommanna Raja K. Evaluation of Similarity measures in a medical image retrieval system. International Journal of Applied Engineering Research. 2014; 9(21):11039–52.
- Zhang G, Ma ZM, Tong Q, He Y, Zhao T. Shape Feature Extraction Using Fourier Descriptors with Brightness in Content-Based Medical Image Retrieval. Int. Conf. Intell. Inf. Hiding Multimed. Signal Process. 2008; p. 71–74.
- Eakins J, Graham M. University of Northumbria at Newcastle: Content-based image retrieval. Technical Report. 1999.
- Fan W, Fox EA, Pathak P, Wu H. The effects of fitness functions on genetic programming-based ranking discovery for web search. JASIST. 2004; 55(7):628-36.
- Shamsi A, Hezamabadi-pour H, Saryazdi S. A short-term learning approach based on similarity refinement in contentbased image retrieval. Multimed Tools Appl. 2014 Sep; 72(2):2025-39.
- Xiaojun Qi, Yutao Han. A novel fusion approach to contentbased image retrieval. Pattern Recognition. 2005 Dec; 38:2449–65.
- Chen Y, Wang J. A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Trans. Pattern. Anal. Mach. Intel. 2002 Sep; 24(9):1252–67.
- Manjunath BS, Salembier P, Sikora T. Introduction to MPEG-7 Multimedia Content. Description Interface. 2002.
- Swain M, Ballard D. Color indexing. Int. J. Computer Vision. 1991 Nov; 7(1):11-32.
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