skip to main content
10.1145/3301506.3301518acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicvipConference Proceedingsconference-collections
research-article

Object Recognition Using Deep Neural Network with Distinctive Features

Published: 29 December 2018 Publication History

Abstract

In this paper, a new object recognition method using statistically weighting Multi-Layer Perceptron (MLP) is proposed. It uses visual distinctive features, which are computed using Bag of Visual Words (BoVW) framework. The proposed method has the following three main steps. At first it represents the images into their respective co-occurrence matrices, which are vectorized using BoVW and gives distinctive features. Then it computes weights from the histograms of visual words for each class. Finally, the statistically weighting distinctive features are applied to the testing image set to find the object class. In the proposed method, we improved MLP by introducing the weighted visual words, which are extracted by sampling the patches from the current image. From the Caltech 256 dataset, four classes namely pedestrians, cars, motorbikes and airplanes are used for the classification accuracy comparison between the MLP based artificial neural network (ANN) and the proposed method. The experimental results show that our method outperforms traditional MLP yielding an average classification accuracy of 89.60%, which is approximately 6.3% more than the compared MLP.

References

[1]
A. Abdullah, R. C. Veltkamp, and M. A. Wiering. Ensembles of novel visual keywords descriptors for image categorization. In Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on, pages 1206--1211. IEEE, 2010.
[2]
Z. Al-Zaydi, B. Vuksanovic, and I. Habeeb. Image processing based ambient context-aware people detection and counting. Int. J. Mach. Learn. Comput.(IJMLC), 8(3):268--273, 2018.
[3]
G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In Workshop on statistical learning in computer vision, ECCV, volume 1, pages 1--2. Prague, 2004.
[4]
Y. R. Devi, S. Sarojini. A survey on machine learning and statistical techniques in bankruptcy prediction. Int. J. Mach. Learn. Comput.(IJMLC), 8(2):268--273, 2018.
[5]
J. Farquhar, S. Szedmak, H. Meng, and J. Shawe-Taylor. Improving "bag-of-keypoints" image categorisation: Generative models and pdf-kernels. 2005.
[6]
L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer vision and Image understanding, 106(1):59--70, 2007.
[7]
R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google's image search. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, volume 2, pages1816--1823. IEEE, 2005.
[8]
G. Griffin, A. Holub, and P. Perona. Caltech-256 object ategory dataset. In California Institute of Technology, 2007.
[9]
H. Han, Q. Han, X. Li, and J. Gu. Hierarchical spatial pyramid max pooling based on sift features and sparse coding for image classification. IET Computer Vision, 7(2):144--150, 2013.
[10]
C. Harris and M. Stephens. A combined corner and edge detector. In Alvey vision conference, volume 15, pages 10--5244. Citeseer, 1988.
[11]
M. Heidarysafa, K. Kowsari, D. E. Brown, K. J. Meimandi, and L. E. Barnes. An improvement of data classification using random multimodel deep learning(rmdl). Int. J. Mach. Learn. Comput.(IJMLC), 8(4):268--273, 2018.
[12]
Z. Ji. Decoupling sparse coding with fusion of fisher vectors and scalable svms for large-scale visual recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 450--457, 2013.
[13]
Y.-G. Jiang, C.-W. Ngo, and J. Yang. 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, pages 494--501. ACM, 2007.
[14]
T. Joachims. A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. Technical report, DTIC Document, 1996.
[15]
S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Computer vision and pattern recognition, 2006 IEEE computer society conference on, volume 2, pages 2169--2178. IEEE, 2006.
[16]
D. D. Lewis. Naive (bayes) at forty: The independence assumption in information retrieval. In European conference on machine learning, pages 4--15. Springer, 1998.
[17]
D. G. Lowe. Local feature view clustering for 3d object recognition. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I-I. IEEE, 2001.
[18]
G. Overett, L. Petersson, N. Brewer, L. Andersson, and N. Pettersson. A new pedestrian dataset for supervised learning. In Intelligent Vehicles Symposium, 2008 IEEE, pages 373--378. IEEE, 2008.
[19]
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. Jawahar. Cats and dogs. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3498--3505. IEEE, 2012.
[20]
F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. In Computer Vision and Pattern Recognition, 2007. CVPR'07.IEEE Conference on, pages 1--8. IEEE, 2007.
[21]
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1--8. IEEE, 2008.
[22]
J. Sivic, A. Zisserman, et al. Video google: A text retrieval approach to object matching in videos. In iccv, volume 2, pages 1470--1477, 2003.
[23]
J. C. Van Gemert, C. J. Veenman, A. W. Smeulders, and J.-M. Geusebroek. Visual word ambiguity. IEEE transactions on pattern analysis and machine intelligence, 32(7):1271--1283, 2010.

Index Terms

  1. Object Recognition Using Deep Neural Network with Distinctive Features

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICVIP '18: Proceedings of the 2018 2nd International Conference on Video and Image Processing
    December 2018
    252 pages
    ISBN:9781450366137
    DOI:10.1145/3301506
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • Kyoto University: Kyoto University
    • TU: Tianjin University

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 December 2018

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Object recognition
    2. bag of visual words
    3. multi-layer perceptron
    4. scale-invariant feature transform
    5. weighting scheme

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICVIP 2018

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 69
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media