Feature Extraction in JPEG domain along with SVM for Content Based Image Retrieval

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Content Based Image Retrieval (CBIR) applies computer vision methods for image retreival purposes from the databases. It is majorly based on the user query, which is in visual form rather than the traditional text form. CBIR is applied in different fields extending from surveillance to remote sensing, E-purchase, medical image processing, security systems to historical research and many others. JPEG, a very commonly used method of lossy compression is used to reduce the size of the image before being stored or transmitted. Almost every digital camera in the market are storing the captured images in jpeg format. The storage industry has seen many major transformations in the past decades while the retrieval technologies are still developing. Though there are some breakthroughs happened in text retrieval, the same is not true for the image and other multimedia retrieval. Specifically image retreival has witnessed many algorithms in the spatial or the raw domain but since majority of the images are stored in the JPEG format, it takes time to decode the compressed image before extracting features and retrieving. Hence, in this research work, we focus on extracting the features from the compressed domain itself and then utilize support vector machines (SVM) for improving the retrieval results. Our proof of concept shows us that the features extracted in compressed domain helps retrieve the images 43% faster than the same set of images in the spatial domain and the accuracy is improved to 93.4% through SVM based feedback mechanism.


  • Keywords


    JPEG (Joint Photographic Experts Group), DCT (Discrete Cosine Transform); CH (Color Histogram); SVM (Support Vector Machine);term frequency–inverse document frequency, Precision and Recall

  • References


      [1] Baisakhi Sur Phadikar, Amit Phadikar and Goutam Kumar Maity, Content-based image retrieval in DCT compressed domain with MPEG-7 edge descriptor and genetic algorithm, G.K. Pattern Anal Applic (2016).

      [2] FazalMalik and BaharumBaharudin, Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain, Journal of King Saud University - Computer and Information Sciences, Volume 25, Issue 2, July 2013, Pages 207-218.

      [3] David Edmundson, Gerald Schaefer, Exploiting JPEG Compression for Image Retrieval, IEEE International Symposium on Multimedia, 2012, DOI: 10.1109/ISM.2012.99.

      [4] Zhe-Ming Lu, Su-Zhi Li and Hans Burkhardt, A Content based image retreival scheme in JPEG compressed domain, International Journal of Innovative Computing, Information and Control, August 2006.

      [5] LokeshSetia, Julia Ick and Hans Burkhardt, SVM-based Relevance Feedback in Image Retrievalusing Invariant Feature Histograms, In Proc. of the IAPR Workshop on Machine Vision Applications, 2005.

      [6] Jun Yue, Li, Liu and Fu. Content-based image retrieval using color and texture fused features.Mathematical and Computer Modelling, Volume 54, Issues 3–4, August 2011, Pages 1121–1127.

      [7] Arnold W. M. Smeulders, Worring, Santini, Amarnath Gupta and Jain, Content-Based Image Retrieval at the End of the Early Years, IEEE Transactions on Pattern Analysis and Machine Intelligence archive, Volume 22 Issue 12, 2000, Page 1349-1380

      [8] RaghupathiGali, M. L. Dewal and R. S. AnandGenetic Algorithm for Content Based Image Retrieval, 4th International Conference onComputational Intelligence, Communication Systems & Networks 2012.

      [9] Hee-Jung Bae and Sung-Hwan Jung, Image retrieval using texture based on DCT, Proceedings of 1997 International Conference onInformation, Communications and Signal Processing, 1997.

      [10] K. Satya Sai Prakash and RMD. Sundaram, Combining Novel features for Content Based Image Retrieval, 14th International Workshop on Systems, Signals and Image Processing, 2007 and 6th EURASIP Conference focused on Speech & Image Processing, Multimedia Communications & Services.

      [11] Ebroul Izuierdo and Qianni Zhang, Senior Member, IEEE, Histology Image Retrieval in Optimized Multifeature Spaces, IEEE Journal of Biomedical and Health Informatics, VOL. 17, NO.1, January 2013

      [12] Padmashri Suresh, R. M. D. Sundaram and Aravindhan Arumugam, Feature Extraction in Compressed Domain for Content Based Image Retrieval, International Conference on Advanced Computer Theory and Engineering, 2008. ICACTE '08.

      [13] Gerald Schaefer, Pixel domain and compressed domain image retrieval features, Eighth International Conference onDigital Information Management (ICDIM), 2013.

      [14] Abhishek, S.N., Shriram K Vasudevan and Sundaram, R.M.D., An enhanced and efficient algorithm for faster, better and accurate edge detection, Communications in Computer and Information Science, Springer Verlag, Volume 679, p.24-41 (2016).

      [15] W. H. Hsu, L. S. Kennedy and S.-F. Chang. Reranking Methods for Visual Search. IEEE Multimedia, vol. 14, no. 3, pp. 14-22, 2007

      [16] B. Chellaprabha and RMD. Sundaram, Effective Video Streaming Using Reset-Controlled DCCP along with Data Quality Analysis, Asian Journal of Information Technology, Year: 2016 | Volume: 15 | Issue: 20 | Page No.: 3986-3990

      [17] Xia Wu,Based on the Characteristics of the JPEG Image Retrieval System Research, Seventh International Conference onMeasuring Technology and Mechatronics Automation (ICMTMA), 2015.

      [18] PrafullaBafna, DhanyaPramodandAnagha Vaidya, Document clustering TF-IDF approach, International Conference onElectrical, Electronics, and Optimization Techniques (ICEEOT), 3-5 March 2016, DOI: 10.1109/ICEEOT.2016.7754750.

      [19] Yu Suzuki, Masahiro Mitsukawa andKyoji Kawagoe, A Image Retrieval Method Using TFIDF Based Weighting Scheme, 19th International Workshop onDatabase and Expert Systems Application, 2008.

      [20] Bin Li, Ng, Xiaolong Li, Tan and Huang, Revealing the Trace of High-Quality JPEG Compression Through Quantization Noise Analysis, IEEE Transactions: Information Forensics & Security, March 2015.

      [21] T. Dharani and I. Laurence Aroquiaraj, A survey on content based image retrieval, International Conference onPattern Recognition, Informatics and Mobile Engineering (PRIME), 2013.

      [22] Wengang Zhou, Houqiang Li and Qi Tian, Recent Advance in Content-based Image Retrieval: A Literature Survey, electronic edition @ arxiv.org, 2017.

      [23] M. B. Rao, B. P. Rao, and A. Govardhan, Content based image retrieval system based on dominant color and texture features,International Journal ofComputer Applications, Vol. 18, 2011 pp. 40-46.

      [24] M. E. ElAlami, A novel image retrieval model based on the most relevant features, Knowledge-Based Systems, Vol. 24, 2011, pp. 23-32.

      [25] R. Ashraf, K. Bashir, A. Irtaza, and M. T. Mahmood, Content based image retrieval using embedded neural networks with bandletized regions, Entropy, Vol. 17, 2015,pp. 3552-3580.

      [26] David Edmundson and Gerald Schaefer, Fast JPEG Image Retrieval using Optimised Huffman Tables, 21st International Conference on Pattern Recognition (ICPR 2012)November 11-15, 2012. Tsukuba, Japan

      [27] Alexandra S. Teynor, Image Retrieval in the Compressed Domain Using JPEG2000, Diploma Thesis for the Multimedia course of study at Department of Computer Science in Augsburg University of Applied Sciences.

      [28] Vimal, S., Kalaivani, L. & Kaliappan, Collaborative approach on mitigating spectrum sensing data hijack attack and dynamic spectrum allocation based on CASG modeling in wireless cognitive radio networks, M. Cluster Computing (2017),https://doi.org/10.1007/s10586-017-1092-0.

      [29] Vimal.s, et al., “Secure data packet transmission in MANET using enhanced identity-based cryptography”. International Journal of New Technologies in Science and Engineering Vol. 3, 2016,No.12, pp.35-42.

      [30] Kannan, N., Sivasubramanian, S., Kaliappan, M. et al. Cluster Comput (2018). https://doi.org/10.1007/s10586-018-2384-8.

      [31] Mariappan, E., Kaliappan, M., Vimal, S.: Energy efficient routing protocol using Grover’s searching algorithm for MANET. Asian J. Inf. Technol. 15, 4986–4994. https://doi.org/10.3923/ajit.2016.4986.4994 (2016)

      [32] Ilango, S.S., Vimal, S., Kaliappan, M. et al. Cluster Comput (2018). https://doi.org/10.1007/s10586-017-1571-3


 

View

Download

Article ID: 11656
 
DOI: 10.14419/ijet.v7i2.19.11656




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.