Object Recognition with Improved Features Extracted from Deep Convolution Networks

  • Authors

    • K Manohar
    • S Irfan
    • K Sravani
    2018-12-13
    https://doi.org/10.14419/ijet.v7i4.39.25675
  • Feature extraction, neural network, object recognition, Machine learning
  • Object identification is a method for identify an exact object in an image. Object identification algorithm depends on matching, learning, and pattern recognition using appearance based feature technique. Object recognition has various method to detect objects it’s include feature extraction and machine learning methods, deep learning search as CNN. The deep learning convolution neural network (CNN) has been proved to be very efficient in feature extraction. CNN is compressed of one or more convolution layers and then follow by one or more totally connected layers. In Image types, the work with classifiers aims at explore the most appropriate types for high level deep features. The feature extracted from the image plays a significant role in image type. It is the process of retrieving the main data from the raw data, its finding the set of parameters that recognize the object uniquely. In feature extraction every nature is represent by a feature vector it’s become identity. In this work the features extracted from CNN applied as input to train machine learning classifiers and perform image classification. A systematic comparison between various classifiers is made for object recognition.

     

     


  • References

    1. [1] G.B. Huang, H. Lee, and E. Learned-Miller, “Learning hierarchical representations for face verification with convolutional deep belief networks,†in Proc. IEEE Int’l Computer Vision and Pattern Recognition, pp. 2518-2525, 2012.

      [2] A. Krizhevsky, I. Sutskever, G.E. Hinton, “Image Net classification with deep convolutional neural networks,†NIPS, 2012

      [3] A. Karpathy , G. Toderici, S. Shetty , and T. Leung, “Large-Scale Video Classification with Convolutional Neural Networks,†in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 1725-1732, 2014.

      [4] A.S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN Features Off-the-Shelf: An Astounding Baseline for Recognition,†in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 512-519, 2014.

      [5] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Accurate Object Detection and Semantic Segmentation,†in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 580-587, 2014.

      [6] K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,†arXiv: 1406.4729.

      [7] Y. Sun, X. Wang, and X. Tang, “Hybrid Deep Learning for Face Verification,†in Proc. IEEE Int’l Conf. Computer Vision, 2013.

      [8] Y. Taigman, M. Yang, M.A. Ranzato, and L. Wolf, “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,†in Proc. IEEE Int’l Computer Vision and Pattern Recognition, 2014.

      [9] E. Zhou, Z. Cao, and Q. Yin, “Naïve-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?†arXiv: 1501.04690, 2015.

      [10] A. Krizhevsky, I. Sutskever, G.E. Hinton, “ImageNet classification with deep convolutional neural networks,†NIPS, 2012.

      [11] D. Ciresan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,†in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 3642-3649, 2012.

      [12] A. Karpathy, G. Toderici, S. Shetty, and T. Leung, “Large-Scale Video Classification with Convolutional Neural Networks,†in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 1725-1732, 2014.

      [13] A.S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN Features Off-the-Shelf: An Astounding Baseline for Recognition,†in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 512-519, 2014.

      [14] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Accurate Object Detection and Semantic Segmentation,†in Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 580-587, 2014. [15] K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,†arXiv: 1406.4729.

      [15] K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun, “What is the best multi-stage architecture for object recognition?†ICCV, pp. 2146-2153, 2009.

      [16] J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, T. Darrell, “DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition,†arXiv: 1310.1531, 2013.

      [17] T. Cover and P. Hart, “Nearest neighbor pattern classification,†IEEE Trans. Information Theory, vol. 13, no. 1, pp. 21-27, 1967.

      [18] V. Vapnik, “Statistical learning theory,†John Wiley: New York, 1998.

      [19] J.A.K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,†Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999.

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  • How to Cite

    Manohar, K., Irfan, S., & Sravani, K. (2018). Object Recognition with Improved Features Extracted from Deep Convolution Networks. International Journal of Engineering & Technology, 7(4.39), 655-659. https://doi.org/10.14419/ijet.v7i4.39.25675