The week's pick
Reseach Article
Cosine Transformed Row and Column Mean Content Based Image Retrieval using Higher Energy Coefficients with Image Tiling and Assorted Similarity Measures
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 112 - Number 13 |
| Year of Publication: 2015 |
| Authors: Sudeep Thepade, Anil Lohar, Payal Dhakate |
10.5120/19725-1365
|
Sudeep Thepade, Anil Lohar, Payal Dhakate . Cosine Transformed Row and Column Mean Content Based Image Retrieval using Higher Energy Coefficients with Image Tiling and Assorted Similarity Measures. International Journal of Computer Applications. 112, 13 ( February 2015), 10-14. DOI=10.5120/19725-1365
Abstract
Content based image retrieval is an application of computer vision technique used to solve the problem of searching images in large databases. The paper introduces three different techniques tested with two different datasets in order to compare the retrieval efficiency and accuracy. The feature vector is formed using Color and texture information extracted. The color information extraction process includes separation of image into Red, Green and Blue planes. Then each plane is divided into 4 blocks and for each block row mean vectors are calculated. This system uses Cosine transform to generate the feature vectors of the query and database images. Cosine transform is applied over a row mean vector of each block separately, which gives a set of feature vector of size 15elementsin the first technique. In the second technique first 60 coefficients are considered for feature vector formation. In the third technique after extraction of Red, Green and Blue components, they are divided into four parts. From each part separate row mean is calculated and discrete cosine transform is applied on it and from each component taking 5 values each block. For each color component. The total feature vector of size 60 is created. Euclidean, Minkowski and Absolute difference are used as similarity measures to compare the image features for image retrieval in proposed CBIR techniques. Two standard datasets namely COIL and Wang are used for the experimentation purpose. COIL dataset consist of 500 processed images in 10 different categories and Wang's dataset consist of 1000 unprocessed images in 10 different categories. Average Precision and Recall values are considered for checking performance of all three techniques.
References
- Dr. H. B. Kekre, Sudeep D. Thepade, Tanuja K. Sarode, Vashali Suryawanshi, "Image Retrieval using Texture Features extracted from GLCM, LBG and KPE", International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 2010, pp1793-8201.
- H. B. kekre, Kavita Sonawane, "Retrieval of Images Using DCT and DCT Wavelet Over Image Blocks", International Journal of Advanced Computer Science and Applications, Vol. 2, No. 10, 2011.
- Dr. H. B. Kekre, Sudeep D. Thepade, Varun K. Banura, "Amelioration of Color Averaging Based Image Retrieval Techniques using Even and Odd parts of Images", International Journal of Engineering Science and Technology Vol. 2(9), 2010, 4238-4246.
- J. Bridget Nirmala, S. Gowri, "A Content based CT Lung Image Retrieval by DCT Matrix and Feature Vector Technique", IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 2, March 2012
- Manimala Singh, K. Hemachandran, "Content Based Image Retrieval using Color and Texture", Signal & Image Processing : An International Journal, 3, Vol No. 1, February 2012 .
- Yixin chen, member IEEE, james z. Wang, member IEEE, and robertkrovetz clue, "Cluster-Based Retrieval Of Images By UnsupervisedLearning", IEEE Transactions On Image Processing, Vol. 14, No. 8,August 2005.
- Qasim Iqbal And J. K. Aggarwal, Cires, "A System For Content-BasedRetrieval In Digital Image Libraries", Seventh International ConferenceOn Control, Automation, Robotics And Vision (Icarcv'02), Dec 2002,Singapore.
- S. Santini and R. Jain, "Similarity measures", IEEE Trans. Pattern Anal. Mach. Intell. , vol. , 2005, in press.
- E. de Ves, A. Ruedin, D. Acevedo, X. Benavent, and L. Seijas, "A New Wavelet-Based Texture Descriptor forImage Retrieval?" , CAIP 2007, LNCS 4673, pp. 895–902, 2007, Springer-Verlag Berlin Heidelberg 2007.
- H. B. Kekre, Dhirendra Mishra, "DCT-DST Plane sectorization of Rowwise Transformed color Images in CBIR" , International Journal of Engineering Science and Technology, Vol. 2 (12), 2010, 7234-7244.
- J. Bridget Nirmala and S. Gowri, "A Content based CT Lung Image Retrieval by DCT Matrix and Feature Vector Technique", International Journal of Computer Science Issues, Vol. 9, Issue 2, No 2, March 2012 ISSN (Online): 1694-0814.
- Ajinkya P. Nilawar, "Image Retrieval Using Gradient operators", Journal on Recent and Innovation trends in Computing and Communication, ISSN 23218169, Vol. 2 Issue 1.
- H. B. Kekre, Tanuja Sarode, Sudeep D. Thepade, "DCT Applied to Row Mean and Column Vectors in Fingerprint Identification", In Proceedings of Int. Conf. on Computer Networks and Security , 27-28 Sept. 2008, VIT, Pune.
- H. B. Kekre, Sudeep D. Thepade, "Image Blending in Vista Creation using Kekre's LUV Color Space", SPIT-IEEEColloquium and Int. Conference, SPIT, Andheri, Mumbai, 04-05 Feb 2008.
- H. B. Kekre, Sudeep D. Thepade, "Boosting Block Truncation Coding using Kekre's LUV Color Space for Image Retrieval", WASET Int.
- H. B. Kekre, Sudeep D. Thepade, "Image Retrieval using Augmented Block Truncation Coding Techniques", ACM Int. Conf. ICAC3-09, 23-24 Jan 2009, FCRCE, Mumbai. Is uploaded on ACM portal.
- H. B. Kekre, Sudeep D. Thepade, "Color Traits Transfer to Grayscale Images", In Proc. of IEEE First International Conference on Emerging Trends in Engg. & Technology, (ICETET-08).
- Dr. H. B. Kekre, Sudeep D. Thepade, Shobhit W. , Miti, K Styajit"Image retrieval with shape features extracted using gradient operators and slope magnitude technique with BTC", Intrenational Journal Of compyter Applications, Vol. 6, Number 8, pp. 28-33, Sept2010.
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