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Probability based Extended Direct Attribute Prediction

by Manju, Ankit Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 155 - Number 5
Year of Publication: 2016
Authors: Manju, Ankit Kumar
10.5120/ijca2016912319

Manju, Ankit Kumar . Probability based Extended Direct Attribute Prediction. International Journal of Computer Applications. 155, 5 ( Dec 2016), 41-44. DOI=10.5120/ijca2016912319

@article{ 10.5120/ijca2016912319,
author = { Manju, Ankit Kumar },
title = { Probability based Extended Direct Attribute Prediction },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 155 },
number = { 5 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 41-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume155/number5/26604-2016912319/ },
doi = { 10.5120/ijca2016912319 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:00:30.539422+05:30
%A Manju
%A Ankit Kumar
%T Probability based Extended Direct Attribute Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 155
%N 5
%P 41-44
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper studies the object recognition along with the direct and indirect attribute prediction. The direct attribute prediction technique has been extended by using the probability based formulae. Moreover, information gain is also used to classify the object into different categories. The information gain is determined by using the entropy. The implementation of the work and comparison with existing DAP technique over YAHOO and pascal dataset signifies the effectiveness of the work.

References
  1. D. G. Lowe, “Distinctive image features from scale-invariant key-points”, International Journal on Computer Vision (IJCV), vol. 60, no. 2, 2004.
  2. C. H. Lampert, H. Nickisch, and S. Harmeling, “Learning to detect unseen object classes by between-class attribute transfer”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
  3. David G. Lowe et. al. ,“Object Recognition from Local Scale-Invariant Features”, Proc. of the International Conference on Computer Vision, Corfu (Sept. 1999).
  4. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
  5. N. Kumar, A. Berg, P. Belhumeur, and S. Nayar ,“Describable visual attributes for face verification and image search”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI),vol. 33, no. 10, pp. 1962–1977, 2011.
  6. K. Yanai and K. Barnard, “Image region entropy: a measure of visualness of web images associated with one concept”, in Proceedings of the 13th annual ACM international conference on Multimedia. ACM, 2005, pp. 419–422.
  7. A. F. Smeaton, P. Over, and W. Kraaij ,“Evaluation campaigns and trecvid”, in ACM Multimedia Information Retrieval, 2006.
  8. D. Parikh and K. Grauman, “Interactively building a discriminative vocabulary of nameable attributes,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 1681–1688.
  9. V. Sharmanska, N. Quadrianto, and C. H. Lampert, “Augmented attribute representations”, in European Conference on Computer Vision (ECCV), 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Dap Iap Probability Sensitivity Specificity.

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