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Object Recognition Using SVM Based Bag of Combined Features

  • Fozia MehboobEmail author
  • Muhammad Abbas
  • Abdul Rauf
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

The evolving and task-dependent nature of visual categories of problems prompts for an example based solutions involving machine learning approach. One such technique, ‘Bag of features’ approach has gained popularity in computer vision applications, including texture recognition, image classification and robot localization. Despite being quite newer, system based on bag of feature method has set performance standards on benchmark of image classification and has achieved drastic scalability in image retrieval. In this paper, a novel approach is being presented for object recognition using combined bag of features and multiclass Support Vector Machine (SVM). Proposed approach presents SVM based combined bag of features method and emphasizes on better recognition and more classification accuracy from images dataset. Although nearest neighbor classifiers have been employed in this area previously, but they suffer from high variance problem in case of limited sampling. Root-SIFT and SURF descriptors are combined for the construction of combined set of bag features, which has reasonable computational complexity yielding excellent results. A Caltech dataset; considered to be very challenging database because of objects are embedded in clutter background having different poses and scales; has been used for testing efficacy of approach. Comparison, made among state-of-the-art approaches shows promising result. Our experiments show state-of-the-art performance on benchmark dataset for object recognition. On Caltech dataset, we achieved a correct classification rate of 65 and 72 at 15 and 30 training images.

Keywords

Scale-invariant-feature-transform (SIFT) Speeded-up-robust-features (SURF) Bag-of-features (Bof) Support vector machine (SVM) Object recognition 

References

  1. 1.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007)CrossRefGoogle Scholar
  2. 2.
    Amato, G., Falchi, F., Bolettieri, P.: Recognizing landmarksusing automated (2010)Google Scholar
  3. 3.
    classification techniques: evaluation of various visual features. In: Second International Conferences on, Advances in Multimedia, pp. 78–83Google Scholar
  4. 4.
    Nanni, L., Lumini, A., Brahnam, S.: Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system. J. King Saud Univ. Sci. 26(2), 89–100 (2014)CrossRefGoogle Scholar
  5. 5.
    Stephen, O., Draper, B.A.: Introduction to the bag of features paradigm for image classification and retrieval. arXiv preprint arXiv:1101.3354 (2011)
  6. 6.
    Gabriella, C., et al.: Visual categorization with bags of keypoints.In: Workshop on statistical learning in computer vision ECCV, vol. 1, pp. 1–22 (2004)Google Scholar
  7. 7.
    Chapelle, O., Haffner, P., Vapnik, V.: SVMs for histogram-based image classification. Trans. Neural Netw. 10(5) (1999)Google Scholar
  8. 8.
    Odone, F., Barla, A., Verri, A.: Building kernels from binary strings for image matching. IEEE Trans. Image Process. 14(2), 169–180 (2005)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Roobaert, D., Van Hulle, M.: View-based 3D object recognition with support vector machines. In: IEEE International Workshop on Neural Networks for Signal Processing, Madison, WI, August 1999Google Scholar
  10. 10.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Vancouver, Canada, July 2001Google Scholar
  12. 12.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(24), 509–522 (2002)CrossRefGoogle Scholar
  13. 13.
    Grauman, K., Darrell, T.: Fast contour matching using approximate earth mover’s distance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington D.C., June 2004Google Scholar
  14. 14.
    Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Nice, October 2003Google Scholar
  15. 15.
    Berg, A., Berg, T., Malik, J.:. Shape matching and object recognition using low distortion correspondences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, June 2005Google Scholar
  16. 16.
    Grauman, K.: Pyramid match kernels: discriminative classification with sets of image features. In: Grauman, K., Darrell, T. Computer Science and Artificial Intelligence Laboratory Technical Report, 2005Google Scholar
  17. 17.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of Seventh International Conference Computer Vision, pp. 1150–1157 (1999)Google Scholar
  18. 18.
    Yuehua, T., et al.: Performance evaluation of SIFT-Based descriptors for object recognition. In: Proceedings of the international multiconference of engineers and computer scientisits, IMECS (2010)Google Scholar
  19. 19.
    Anitha, J.J., Deepa, S.M.: Tracking and recognition of objects using SURF descriptor and harris corner detection. Int. J. Curr. Eng. Technol 4(2), 775–778 (2014)Google Scholar
  20. 20.
    Muralidharan, R., Chandrasekar, C.: Object recognition using support vector machine augmented by RST invariants. Int. J. Comput. Sci. Issues (IJCSI) 8(5), 280–286 (2011)Google Scholar
  21. 21.
    Frederic, J., Triggs, B.: Creating efficient codebooks for visual recognition. In: Tenth IEEE International Conference on Computer Vision ICCV 2005, vol. 1. IEEE (2005)Google Scholar
  22. 22.
    Zhang, J., et al.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)CrossRefGoogle Scholar
  23. 23.
    Mohamed, A., Munich, M., Perona, P.: Bag of words for large scale object recognition. In: Computational Vision Lab, Caltech, Pasadena, CA, USAGoogle Scholar
  24. 24.
    Grauman, K., Darrell, T.: Discriminative classification with sets of image features. In: ICCV (2005)Google Scholar
  25. 25.
    Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondence. In: CVPR (2005)Google Scholar
  26. 26.
    Holub, A.D., Welling, M., Perona, P.: Combining generative models and _sher kernels for object recognition. In: ICCV (2005)Google Scholar
  27. 27.
    Hao, Z., et al.: SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2. IEEE (2006)Google Scholar
  28. 28.
    Marin-Jimenez, M.J., De La Blanca, N.P.: Empirical study of multi-scale filter banks for object categorization. In: 18th International Conference on Pattern Recognition ICPR 2006, vol. 1. IEEE (2006)Google Scholar
  29. 29.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE CVPR 2003, vol. 2, pp. 264–271, Feburary 2003Google Scholar
  30. 30.
    Jim, M., Lowe, D.G.: Multiclass object recognition with sparse, localized features. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE (2006)Google Scholar
  31. 31.
    Svetlana, L., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE (2006)Google Scholar
  32. 32.
    Stephen, O., Draper, B.A.: Introduction to the bag of features paradigm for image classification and retrieval. arXiv preprint arXiv:1101.3354 (2011)
  33. 33.
    Wang, G., Ye, Z., Li, F.-F.: Using dependent regions for object categorization in a generative framework. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE (2006)Google Scholar
  34. 34.
    Tsai, C.-F.: Bag-of-words representation in image annotation: a review. ISRN Artificial Intelligence (2012)Google Scholar
  35. 35.
    Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. J. Mach. Learn. Res. 8, 725–760 (2007)zbMATHGoogle Scholar
  36. 36.
    Dardas, N.H., Georganas, N.D.: Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans. Instrum. Measur. 60(11), 3592–3607 (2011)CrossRefGoogle Scholar
  37. 37.
    David, A., et al.: Efficient object pixel-level categorization using bag of features. In: Advances in Visual Computing. Springer, Heidelberg, pp. 44–54 (2008)Google Scholar
  38. 38.
    Cordelia, S.: Bag-of-features for category classification. In: ENS/INRIA Visual Recognition and Machine Learning Summer School Lecture, 25–29 July 2011Google Scholar
  39. 39.
    Sezer, O.G., Ercil, A., Keskinoz, M.: Subspace based object recognition using support vector machines. In: 2005 13th European Signal Processing Conference. IEEE (2005)Google Scholar
  40. 40.
    Nguyen, T., et al.: Object detection using scale invariant feature transform. In: Genetic and Evolutionary Computing, pp. 65–72. Springer (2014)Google Scholar
  41. 41.
    Object Recognition using SIFT, Shivakanth, International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 4, June 2014Google Scholar
  42. 42.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3360–3367. IEEE (2010)Google Scholar
  43. 43.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009, pp. 1794–1801, June 2009Google Scholar
  44. 44.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8. IEEE, October 2007Google Scholar
  45. 45.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2006, vol. 2, pp. 2169–2178. IEEE (2006)Google Scholar
  46. 46.
    Jiang, Y.G., Ngo, C.W., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 494–501. ACM, July 2007Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National University of Sciences and TechnologyIslamabadPakistan
  2. 2.Department of Computer ScienceAl-Imam Mohammad Ibn Saud Islamic University (IMSIU)RiyadhKingdom of Saudi Arabia

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