Improved HOG Descriptors in Image Classification with CP Demonstration

Tan Vo, Dat TRAN, Wanli MA, Khoa NGUYEN

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    2 Citations (Scopus)

    Abstract

    Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.
    Original languageEnglish
    Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2013)
    Subtitle of host publicationLecture Notes in Computer Science
    EditorsMinho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil
    Place of PublicationGermany
    PublisherSpringer
    Pages384-391
    Number of pages8
    Volume8228
    ISBN (Electronic)9783642420511
    ISBN (Print)9783642420504
    DOIs
    Publication statusPublished - 2013
    Event20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of
    Duration: 3 Nov 20137 Nov 2013

    Conference

    Conference20th International Conference on Neural Information Processing (ICONIP 2013)
    Abbreviated titleICONIP 2013
    CountryKorea, Republic of
    CityDaegu
    Period3/11/137/11/13

    Fingerprint

    Image classification
    Demonstrations
    Computer vision
    Tensors
    Support vector machines
    Decomposition
    Experiments

    Cite this

    Vo, T., TRAN, D., MA, W., & NGUYEN, K. (2013). Improved HOG Descriptors in Image Classification with CP Demonstration. In M. Lee, A. Hirose, Z-G. Hou, & R. M. Kil (Eds.), International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science (Vol. 8228, pp. 384-391). Germany: Springer. https://doi.org/10.1007/978-3-642-42051-1_48
    Vo, Tan ; TRAN, Dat ; MA, Wanli ; NGUYEN, Khoa. / Improved HOG Descriptors in Image Classification with CP Demonstration. International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. editor / Minho Lee ; Akira Hirose ; Zeng-Guang Hou ; Rhee Man Kil. Vol. 8228 Germany : Springer, 2013. pp. 384-391
    @inproceedings{4cc43f3cb40346a0a3e2745cced591cb,
    title = "Improved HOG Descriptors in Image Classification with CP Demonstration",
    abstract = "Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.",
    keywords = "Age and Gender Classification, Tensor Decomposition, histogram of oriented gradients",
    author = "Tan Vo and Dat TRAN and Wanli MA and Khoa NGUYEN",
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    Vo, T, TRAN, D, MA, W & NGUYEN, K 2013, Improved HOG Descriptors in Image Classification with CP Demonstration. in M Lee, A Hirose, Z-G Hou & RM Kil (eds), International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. vol. 8228, Springer, Germany, pp. 384-391, 20th International Conference on Neural Information Processing (ICONIP 2013), Daegu, Korea, Republic of, 3/11/13. https://doi.org/10.1007/978-3-642-42051-1_48

    Improved HOG Descriptors in Image Classification with CP Demonstration. / Vo, Tan; TRAN, Dat; MA, Wanli; NGUYEN, Khoa.

    International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. ed. / Minho Lee; Akira Hirose; Zeng-Guang Hou; Rhee Man Kil. Vol. 8228 Germany : Springer, 2013. p. 384-391.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AB - Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.

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    Vo T, TRAN D, MA W, NGUYEN K. Improved HOG Descriptors in Image Classification with CP Demonstration. In Lee M, Hirose A, Hou Z-G, Kil RM, editors, International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. Vol. 8228. Germany: Springer. 2013. p. 384-391 https://doi.org/10.1007/978-3-642-42051-1_48