Total views : 174
Image Retrieval using Generalized Gaussian Distribution and Score based Support Vector Machine
Objectives: Retrieving images from huge volumes of image database has its application in broad areas like medicine, agriculture, military etc. Annotation based approaches have become obsolete because they are time consuming and cannot describe the image effectively. The rich content in the images can overcome the limitations of annotation based techniques. Texture is the most vital visual cue used to analyze images. Methods: In the proposed technique, the image texture features are statistically represented using Generalized Gaussian Distribution in the wavelet domain. A linear score based Support Vector Machine is incorporated to identify analogous patterns to the query image from the database. Findings: The efficacy of the proposed algorithm is ascertained by conducting extensive experiments. Two texture image database of size 1400 and 1920 is used for our experiment. The proposed algorithm is verified in terms of average recall performance against the standard benchmark algorithms. It is observed that the proposed score based SVM yields higher precision and flexibility in separating the similarity within the classes and dissimilarity across different classes. Improvements/Applications: Compared to the traditional approaches, the retrieval rate of this method is improved by 30% at a considerably low computational complexity.
Generalized Gaussian Distribution, Support Vector Machine, Texture Retrieval.
- Babu RB, Vanitha V, Anish KS. Content based image retrieval using color, texture, shape and active re-ranking method. Indian Journal of Science and Technology. 2016 May; 9 (17):1–5.
- Khan SMH, Hussain A. A hybrid approach to content based image retrieval using computational intelligence techniques.Indian Journal of Science and Technology. 2016 Jun; 9(21):1–8.
- Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973 Nov; SMC-3(6):610–21.
- Carr JR, De Miranda RP. The semivariogram in comparison to the co-occurrence matrix for classification of image texture. IEEE Transactions on Geoscience and Remote Sensing. 1998 Nov; 36(6):1945–52.
- Comparison and fusion of co-occurrence, Gabor and MRF texture features for classification of SAR sea ice imagery.Available from: http://www.tandfonline.com/doi/pdf/10.1 080/07055900.2001.9649675
- Sucharita V, Jyoti S, Mamatha DM. Texture feature extraction for the classification of penaeid prawn species using gabor filter. Indian Journal of Science and Technology. 2015 Aug; 8(17):1–4.
- Clausi DA, Yue B. Comparing co occurrence probabilities and Markov random fields for texture analysis. IEEE Transactions on Geoscience and Remote Sensing. 2004 Jan; 42(1):215–28.
- Dekker RJ. Texture analysis and classification of ERS SAR images for MAP updating of urban areas in the Nehterlands.IEEE Transactions on Geoscience and Remote Sensing.2003 Sep; 41(9):1950–8.
- Novak LM, Owirka GJ, Netishen CM. Performance of a high-resolution polarimetric SAR automatic target recognition system. Lincoln Laboratory Journal. 1993; 6(1):11–23.
- Use of SAR image texture in terrain classification. Available from: http://ieeexplore.ieee.org/document/606390/
- Abbadeni N. Computational perceptual features for texture representation and retrieval. IEEE Transactions on Image Processing. 2011 Jan; 20(1):236–46.
- Kandaswamy U, Adjeroh A. Efficient texture analysis of SAR imagery. IEEE Transactions on Geoscience and Remote Sensing. 2005 Sep; 43(9):2075–83.
- Chamundeeswari VV, Singh D, Singh K. An analysis of texture measures in PCA-based unsupervised classification of SAR Images. IEEE Geoscience and Remote Sensing Letters. 2009 Apr; 6(2):214–8.
- Li L, Tong CS, Choy SK. Texture classification using refined histogram. IEEE Transactions on Image Processing. 2010 May; 19(5):1371–8.
- Singha M, Hemachandran K, Paul A. Content-based image retrieval using the combination of the fast wavelet transformation and the color histogram. IET Image Processing.2012 Dec; 6(9):1221–6.
- Choy SK, Tong CS. Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval. IEEE Transactions on Image Processing. 2010 Feb; 19(2):281–9.
- Choy SK, Tong CS. Statistical properties of bit-plane probability model and its application in supervised texture classification. IEEE Transactions on Image Processing.2008 Aug; 17(8):1399–405.
- Yuan H, Zhang XP. Statistical modeling in the wavelet domain for compact feature extraction and similarity measure of images. IEEE Transactions on Circuits and Systems for Video Technology. 2010 Mar; 20(3):439–45.
- Atto AM, Berthoumieu Y, Bolon P. 2-D wavelet packet spectrum for texture analysis. IEEE Transactions on Image Processing. 2013 Jun; 22(6):2495–500.
- Transform features for texture classification and discrimination in large image databases. Available from: http:// ieeexplore.ieee.org/document/413817/
- Unser M. Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing.1995 Nov; 4(11):1549–60.
- Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis Machine Intelligence. 1996 Aug; 18(8):837–42.
- Wouwer GV, Scheunders P, Dyck DV. Statistical texture characterization from discrete wavelet representations.IEEE Transactions on Image Processing. 1999 Apr; 8(4):592–8.
- Do MN, Vetterli N. Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance.IEEE Transactions on Image Processing. 2002 Feb; 11(2):146–58.
- Kundu MK, Chowdhury M, Banerjee M. Interactive image retrieval with wavelet features. Springer Berlin Heidelberg; 2011 Jul. p. 167–72.
- Rui Y, Huang TS, Ortega M, Mehrotra S. Relevance feedback: A power tool for interactive content based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology. 1998 Sep; 8(5):644–55.
- Hsu C, Li C. Relevance feedback using generalized bayesian framework with region based optimization learning. IEEE Transactions on Image Processing. 2005 Oct; 14(10):1617– 31.
- Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989 Jul; 11(7):674–93.
- Sharifi K, Leon-Garcia A. Estimation of shape parameter for generalized gaussian distributions in subband decompositions of video. IEEE Transactions on Circuit, System, and Video Technologies. 1995 Feb; 5(1):52–6.
- Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors. Available from: http://www2.parc.com/spl/members/jjliu/publication/ ITMoulin.pdf
- Vapnik V. Statistical learning theory. New York: WileyInterscience; 1998.
- Cristianini N, Shawe-Taylor J. An introduction to support vector machines (and other kernel based learning methods).Cambridge, UK: Cambridge Univ Press; 2000.
- Cortes C, Vapnik V. Support-vector network. Machine Learning. 1995; 20:273–97.
- Chapelle O, Haffner P, Vapnik V. SVMs for histogram based image classification. IEEE Transactions on Neural Networks. 1999; 1–29.
- Linear spatial pyramid matching using sparse coding for image classification. Available from: http://web.eecs.umich.edu/~silvio/teaching/EECS598_2010/slides/10_12_Byungsoo.pdf
- Zegarra JAM, Leite NJ, Torres RS. Rotation-invariant and scale invariant steerable pyramid decomposition for texture image retrieval. SIBGRAPI’07 Proc of the XX Brazilian Symposium on Computer Graphics and Image Processing; 2007 Oct. p. 121–8.
- Texture classification test suites. Available from: http:// www.outex.oulu.fi/index.php?page=classification
- Nearest neighbor classifier based on nearest feature decisions.Available from: http://comjnl.oxfordjournals.org/ content/early/2012/01/31/comjnl.bxs001.abstract
- Govindaraju S, Kumar GPR. A novel content based medical image retrieval using SURF features. Indian Journal of Science and Technology. 2016 May; 9(20):1–8.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.