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ACSIR: ANOVA Cosine Similarity Image Recommendation in vertical search

  • D. SejalEmail author
  • T. Ganeshsingh
  • K. R. Venugopal
  • S. S. Iyengar
  • L. M. Patnaik
Regular Paper
  • 157 Downloads

Abstract

In today’s world, online shopping is very attractive and grown exponentially due to revolution in digitization. It is a crucial demand to provide recommendation for all the search engine to identify users’ need. In this paper, we have proposed a ANOVA Cosine Similarity Image Recommendation (ACSIR) framework for vertical image search where text and visual features are integrated to fill the semantic gap. Visual synonyms of each term are computed using ANOVA p value by considering image visual features on text-based search. Expanded queries are generated for user input query, and text-based search is performed to get the initial result set. Pair-wise image cosine similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain-specific site. Experiment results show that the ACSIR outperforms iLike method by providing more relevant products to the user input query.

Keywords

Content-based image retrieval (CBIR) Image recommendation Vertical search 

References

  1. 1.
    Idrissi N, Martinez J, Aboutajdine D (2009) Bridging the semantic gap for texture-based image retrieval and navigation. J Multimed 4(5):277–283CrossRefGoogle Scholar
  2. 2.
    Feng F, Wang C, Yao Y, Deng K, Zhang L, Ma WY (2006) IGroup: a web image search engine with semantic clustering of search results. In: Proceedings of the 14th annual ACM international conference on multimedia, pp 497–498Google Scholar
  3. 3.
    Ma H, Zhu J, Lyu MRT, King I (2010) Bridging the semantic gap between image contents and tags. IEEE Trans Multimed 12(5):462–473CrossRefGoogle Scholar
  4. 4.
    Luo B, Wang X, Tang X (2003) World Wide Web based image search engine using text and image content features. Electron Imaging 2003:123–130Google Scholar
  5. 5.
    Cui J, Wen F, Tang X (2008) Real time google and live image search re-ranking. In: Proceedings of the 16th ACM international conference on multimedia, pp 729–732Google Scholar
  6. 6.
    Sejal D, Abhishek D, Venugopal KR, Iyengar SS, Patnaik LM (2016) IR_URFS_VF: image recommendation with user relevance feedback session and visual features in vertical image search. Int J Multimed Inf Retr 5(4):255–264CrossRefGoogle Scholar
  7. 7.
    Gudivada VN, Raghavan VV (1995) Content based image retrieval systems. Computer 28(9):18–22CrossRefGoogle Scholar
  8. 8.
    Zachary JM, Iyengar SS (1999) Content based image retrieval systems. In: Proceedings of IEEE symposium on application-specific systems and software engineering and technology, pp 136–143Google Scholar
  9. 9.
    Stanchev PL (2001) Content-based image retrieval systems. In: Proceedings of CompSysTech’2001, p 1Google Scholar
  10. 10.
    TANG LJ, DUAN L, GAO W (2001) Content based image retrieval system. Appl Res Comput 7:1–4Google Scholar
  11. 11.
    Smeulders AWM, Worring M, Santini S, Gupta A, Jain. Ramesh (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380CrossRefGoogle Scholar
  12. 12.
    Datta R, Li J, Wang JZ (2005) Content-based image retrieval: approaches and trends of the new age. In: Proceedings of the 7th ACM SIGMM international workshop on multimedia information retrieval, pp 253–262Google Scholar
  13. 13.
    Akakin HC, Gurcan MN (2012) Content-based microscopic image retrieval system for multi-image queries. IEEE Trans Inf Technol Biomed 16(4):758–769CrossRefGoogle Scholar
  14. 14.
    Ramachandra A, Abhilash S, Raja KB, Venugopal KR (2012) Feature level fusion based bimodal biometric using transformation domine techniques. IOSR J Comput Eng (IOSRJCE) 3(3):39–46CrossRefGoogle Scholar
  15. 15.
    Lavanya BN, Raja KB, Venugopal KR, Patnaik LM (2009) Minutiae extraction in fingerprint using gabor filter enhancement. In: International conference on advances in computing, control, and telecommunication technologies, pp 54–56Google Scholar
  16. 16.
    Akbas E, Vural FTY (2007) Automatic Image Annotation by Ensemble of Visual Descriptors. In: Proceedings of CVPR’07, IEEE conference on computer vision and pattern recognition, pp 1–8Google Scholar
  17. 17.
    Bartolini I, Ciaccia P (2010) Multi-dimensional keyword-based image annotation and search. In: Proceedings of the \(2{nd}\) international workshop on keyword search on structured data, pp 5–10Google Scholar
  18. 18.
    Wang C, Jing F, Zhang L, Zhang HJ (2007) Content-based image annotation refinement. In: Proceedings of CVPR’07, IEEE conference on computer vision and pattern recognition, pp 1–8Google Scholar
  19. 19.
    Li J, Wang JZ (2008) Real-time computerized annotation of pictures. IEEE Trans Pattern Anal Mach Intell 30(6):985–1002MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wang C, Jing F, Zhang L, Zhang HJ (2006) Image annotation refinement using random walk with restarts. In: Proceedings of the \(14{th}\) annual ACM international conference on multimedia, pp 647–650Google Scholar
  21. 21.
    Makadia A, Pavlovic V, Kumar S (2008) A new baseline for image annotation. Comput Vis ECCV 2008:316–329Google Scholar
  22. 22.
    Verma Y, Jawahar CV (2012) Image annotation using metric learning in semantic neighbourhoods. Comput Vis ECCV 2012:836–849Google Scholar
  23. 23.
    Wang C, Blei D, Li FF (2009) Simultaneous image classification and annotation. In: Proceedings of CVPR 2009, IEEE conference on computer vision and pattern recognition, pp 1903–1910Google Scholar
  24. 24.
    Krapac J, Allan M, Verbeek J, Jurie F (2010) Improving web image search results using query-relative classifiers. IEEE Conf Comput Vis Pattern Recognit (CVPR) 2010:1094–1101Google Scholar
  25. 25.
    Ben-Haim N, Babenko B, Belongie S (2006) Improving web-based image search via content based clustering. In: Proceedings of international conference on computer vision and pattern recognition workshop, p 106Google Scholar
  26. 26.
    Tang X, Liu K, Cui J, Wen F, Xiaogang Wang (2012) Intentsearch: capturing user intention for one-click internet image search. IEEE Trans Pattern Anal Mach Intell 34(7):1342–1353CrossRefGoogle Scholar
  27. 27.
    Fan J, Keim DA, Gao Y, Luo H, Li Zongmin (2009) JustClick: personalized image recommendation via exploratory search from large-scale flickr images. IEEE Trans Circuits Syst Video Technol 19(2):273–288CrossRefGoogle Scholar
  28. 28.
    Chen Y, Yu N, Luo B, Chen X (2010) iLike: integrating visual and textual features for vertical search. In: Proceedings of the international conference on multimedia, pp 221–230Google Scholar
  29. 29.
    Chen Y, Sampathkumar H, Luo B, Chen XW (2013) iLike: bridging the semantic gap in vertical image search by integrating text and visual features. IEEE Trans Knowl Data Eng 25(10):2257–2270CrossRefGoogle Scholar
  30. 30.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621CrossRefGoogle Scholar
  31. 31.
    Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–473CrossRefGoogle Scholar
  32. 32.
    Moreno P, Bernardino A, Santos-Victor J (2005) Gabor parameter selection for local feature detection. In: Marques JS, Pérez de la Blanca N, Pina P (eds) Pattern recognition and image analysis. IbPRIA 2005. Lecture notes in computer science, vol 3522. Springer, Berlin, HeidelbergGoogle Scholar
  33. 33.
    Derrac J, García S, Herrera F (2015) JavaNPST: nonparametric statistical tests in Java. arXiv preprint arXiv:1501.04222
  34. 34.
    Math C (2016) The apache commons mathematics library. https://commons.apache.org/proper/commonsmath/

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • D. Sejal
    • 1
    • 2
    Email author
  • T. Ganeshsingh
    • 1
  • K. R. Venugopal
    • 1
  • S. S. Iyengar
    • 3
  • L. M. Patnaik
    • 4
  1. 1.Department of Computer Science and EngineeringUniversity Visvesvaraya College of Engineering, Bangalore UniversityBangaloreIndia
  2. 2.T. John Institute of TechnologyBangaloreIndia
  3. 3.Florida International UniversityMiamiUSA
  4. 4.National Institute of Advanced StudiesBangaloreIndia

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