Abstract

Content Based Image Retrieval is a way of computer viewing technique used to retrieve digital images from a huge database. In this paper we have first calculated the feature vector column-wise and row-wise separately. After this we have concatenated the feature vectors of column-wise and row-wise. To evaluate the performance of the proposed method we have used Precision-Recall crossover point, LIRS, LSRR and LSRI. Sum of Absolute Distance and Euclidean Distance are the two similarity measures used. The column-row wise DCT transformed image is sectorized on the basis of even-odd column components of transformed image with augmentation of zero and highest row components. The proposed algorithm is applied to a database of thousand images. These thousand images are grouped in ten different classes. Performance is evaluated and compared for 4, 8, 12, 16 DCT sectors.

References

• P. S. Hiremath, Jagadeesh Pujari, "Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement". *
• MarjoMarkkula, Marius Tico, BemmuSepponen, KatjaNirkkonen and EeroSormunen, "A Test Collection for the Evaluation of Content-Based Image Retrieval Algorithms - A User and Task-Based Approach", Published in Information Retrieval 4(3/4), 275-294 (2001). *
• H. B. Kekre, Tanujasarode, VinayaRawool, "Finger Print Identification using Discrete Sine Transform (DST)" International Conference on Advanced Computing & Communication Technology (ICACCT-2008) Asia Pacific Institute of Information Technology, Panipat India 8-9 Nov 2008H. B. Kekre, Dhirendra Mishra, "DCT Sectorization for Feature Vector Generation in CBIR" International Journal of Computer Applications (0975 – 8887) Volume 9– No. 1, November 2010.
• Dr. H. B. Kekre, Dhirendra Mishra, "Sectorization of Walsh and Walsh Wavelet in CBIR", International Journal on Computer Science and Engineering (IJCSE) Vol. 3 No. 6 June 2011. *
• H. B. Kekre, SudeepThepade, Juhi Jain and NamanAgrawal, "IRIS Recognition using Texture Features Extracted from Haarlet Pyramid", International Journal of Computer Applications (IJCA) Vol. 11, No. 12, pp. 01-05, December, 2010. .
• H. B. Kekre, Kamal Shah, "Application of DCT row and column feature vector for face recognition with comparison to full DCT and PCA", International Journal of Computer Applications in Engineering, Technology and Science (IJ-CA-ETS) , Vol. 1, No. 2, 435-439 April/September 2009.
• H. B. Kekre, Dhirendra Mishra, "DCT Sectorization for Feature Vector Generation in CBIR" International Journal of Computer Applications (0975 – 8887) Volume 9– No. 1, November 2010.
• H. B. Kekre, Dhirendra Mishra, "DCT-DST Plane sectorization of Row-wise Transformed color Images in CBIR", International Journal of Engineering Science and Technology Vol. 2 (12), 2010,7234-7244.
• H. B. Kekre, Dhirendra Mishra, "Sectorization of Haar and Kekre's Wavelet for feature extraction of color images in image retrieval", International journal of computer science and information security (IJCSIS), USA, Vol. 9, No. 2, Feb 2011, pp. 180-188.
• James Z. Wang, Jia Li, GioWiederhold, ``SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries,'' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 23, no. 9, pp. 947-963, 2001.
• Jia Li, James Z. Wang, ``Automatic linguistic indexing of pictures by a statistical modeling approach,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, 2003.
• H. B. Kekre, Dhirendra Mishra, Chirag Thakkar, "Column wise DCT plane sectorization in CBIR," International Journal of Computer Science and Information Technologies (IJCSIT), vol. 3 no. 1, pp. 3229-3235, 2012.
• S. Nandgopalan, Dr. B. S. Adiga , N. Deepak, "A Universal Model for Content-Based Image Retrieval", World Academy of Science, Engineering and Technology 46 2008.
• I. Cox, M. Miller, T. Minka, T. Papa Thomas, and P. Finials, "The Bayesian image retrieval system, PicHunter: Theory, implementation and psychophysical experiments," IEEE Transactions on ImageProcessing. vol. 9, no. 1, pp. 20–37, 2000.
• H. B. Kekre, Sudeep D. Thepade, "Using YUV Color Space to Hoist the Performance of Block Truncation Coding for Image Retrieval", IEEE International Advanced Computing Conference 2009 (IACC'09), Thapar University, Patiala, INDIA, 6-7 March 2009.
• H. B. Kekre, Sudeep D. Thepade, "Image Retrieval using Augmented Block Truncation Coding Techniques", ACM International Conference on Advances in Computing, Communication and Control (ICAC3-2009), pp. : 384-390, 23-24 Jan 2009, Fr. Conceicao Rodrigous College of Engg. , Mumbai .
• P. S. Suhasini, Dr. K. Sri Rama Krishna, Dr. I. V. Murali Krishna, "CBIR using color histogram processing", Journal of Theoretical and Applied Information Technology, vol. 6 no. 1 (pp 116-122).
• S. Nandgopalan, Dr. B. S. Adiga, N. Deepak, "A Universal Model for Content-Based Image Retrieval", World Academy of Science, Engineering and Technology 46 2008.
• Ryszard S. Choras "Feature Extraction for CBIR and Biometrics applications", 7th WSEAS International Conference on Applied Computer Science, Venice, Italy,November 21-23 2007.
• Neetu Sharma, PareshRawat, Jaikaran Singh, "Efficient CBIR Using Color Histogram Processing", Signal & Image Processing: An International Journal Vol. 2, No. 1, March 2011