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Quantized Local Trio Patterns for Multimedia Image Retrieval System

  • P. RohiniEmail author
  • C. Shoba Bindu
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)

Abstract

In this paper, we propose a novel feature descriptor named quantized local trio pattern (QLTP) for multimedia image retrieval application. The QLTP extracts quantized edge information from the pixels in a specified neighborhood. QLTP integrates the quantization and trio patterns for image retrieval. Performance of the QLTP is evaluated by conducting experiments on Corel-10,000 databases. Experimental results exhibit an improvement in terms of avg. retrieval precision (ARP) and avg. retrieval rate (ARR) as compared to the other related methods.

Keywords

Retrieval Local binary patterns Avg. retrieval precision Avg. retrieval rate Database 

References

  1. 1.
    Jégou H, Perronnin F, Douze M, Sanchez J, Perez P, Schmid C (2012) Aggregating local image descriptors into compact codes. IEEE Trans Pattern Anal Mach Intell 34(9):1704–1716CrossRefGoogle Scholar
  2. 2.
    Tsikrika T, Popescu A, Kludas J (2011) Overview of the Wikipedia image retrieval task at ImageCLEF 2011. In: The working notes for the CLEF 2011 labs and workshop, Amsterdam, The Netherlands, 19–22 Sept 2011Google Scholar
  3. 3.
    Rahimi, M, Moghaddam E (2015) A content based image retrieval system based on color ton distribution descriptors. SIViP 9:691.  https://doi.org/10.1007/s11760-013-0506-6CrossRefGoogle Scholar
  4. 4.
    Simou N, Athanasiadis T, Stoilos G et al (2008) Image indexing and retrieval using expressive fuzzy description logics. SIViP 2:321.  https://doi.org/10.1007/s11760-008-0084-1CrossRefGoogle Scholar
  5. 5.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  6. 6.
    Everingham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, Zisserman A (2015) The PASCAL visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136CrossRefGoogle Scholar
  7. 7.
    Kokare M, Biswas PK, Chatterji BN (2007) Texture image retrieval using rotated wavelet filters. J Pattern Recogn Lett 28:1240–1249CrossRefGoogle Scholar
  8. 8.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  9. 9.
    Takala V, Ahonen T, Pietikainen M (2005) Block-based methods for image retrieval using local binary patterns. In: SCIA 2005, LNCS, vol 3450, pp 882–891CrossRefGoogle Scholar
  10. 10.
    Yao C-H, Chen S-Y (2003) Retrieval of translated, rotated and scaled color textures. Pattern Recogn 36:913–929CrossRefGoogle Scholar
  11. 11.
    Vipparthi SK, Nagar SK (2016) Local extreme complete trio pattern for multimedia image retrieval system. Int J Autom Comput 13:457.  https://doi.org/10.1007/s11633-016-0978-2CrossRefGoogle Scholar
  12. 12.
    Koteswara Rao L, Venkata Rao D (2015) Local quantized extrema patterns for content-based natural and texture image retrieval. Hum Cent Comput Inf Sci 5:26.  https://doi.org/10.1186/s13673-015-0044-zCrossRefGoogle Scholar
  13. 13.
    Corel 1000 and Corel 10000 image database [Online]. Available: http://wang.ist.psu.edu/docs/related.shtml

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CSE, Faculty of Science and Tech.IFHEHyderabadIndia
  2. 2.Department of CSEJNTUAAnantpuramuIndia
  3. 3.Department of CSEJNTUACEAAnantpuramuIndia

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