Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images

The usage of computer vision adds a new paradigm in the field of animal biometric, and has recently received more attention due to the growing importance of identification and tracking of animal species or individual animals. Biometric characteristics help to develop a better representation and a be...

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Veröffentlicht in:	International journal of parallel programming Jg. 46; H. 5; S. 812 - 837
Hauptverfasser:	Kumar, Santosh, Singh, Sanjay Kumar, Abidi, Ali Imam, Datta, Deepanwita, Sangaiah, Arun Kumar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2018 Springer Nature B.V
Schlagworte:	Animals Biometrics Cattle Computer Science Computer vision Face recognition Feature extraction Feature recognition Image classification Machine learning Object recognition Processor Architectures Regression models Representations Software Engineering/Programming and Operating Systems Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm Theory of Computation Texture descriptor Multi-modal SRC Cattle recognition Animal biometrics Fusion Muzzle point pattern
ISSN:	0885-7458, 1573-7640
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Abstract	The usage of computer vision adds a new paradigm in the field of animal biometric, and has recently received more attention due to the growing importance of identification and tracking of animal species or individual animals. Biometric characteristics help to develop a better representation and a better identification of different animal species and individual animals. In this work, we propose an effective approach for automatic cattle recognition based on the multiple features of muzzle points and the cattle face images. The proposed method deals the cattle recognition problem as a classification problem among the multiple linear regression models and provides a new theory for the recognition of individual cattle. The group sparse signal representation based classification offers the key to addressing this problem using L2-minimization. In this paper, a comparative study among the well-established handcrafted texture feature extraction techniques and the appearance-based feature extraction techniques is also presented. A detailed set of experimental results on muzzle point image database is also carried to prove the theory. Our method has achieved 93.87% identification accuracy which demonstrates the superiority of the proposed method than the other existing machine learning based recognition algorithms.
AbstractList	The usage of computer vision adds a new paradigm in the field of animal biometric, and has recently received more attention due to the growing importance of identification and tracking of animal species or individual animals. Biometric characteristics help to develop a better representation and a better identification of different animal species and individual animals. In this work, we propose an effective approach for automatic cattle recognition based on the multiple features of muzzle points and the cattle face images. The proposed method deals the cattle recognition problem as a classification problem among the multiple linear regression models and provides a new theory for the recognition of individual cattle. The group sparse signal representation based classification offers the key to addressing this problem using L2-minimization. In this paper, a comparative study among the well-established handcrafted texture feature extraction techniques and the appearance-based feature extraction techniques is also presented. A detailed set of experimental results on muzzle point image database is also carried to prove the theory. Our method has achieved 93.87% identification accuracy which demonstrates the superiority of the proposed method than the other existing machine learning based recognition algorithms.
Author	Abidi, Ali Imam Datta, Deepanwita Kumar, Santosh Singh, Sanjay Kumar Sangaiah, Arun Kumar
Author_xml	– sequence: 1 givenname: Santosh surname: Kumar fullname: Kumar, Santosh organization: Computer Science and Engineering, IIIT – sequence: 2 givenname: Sanjay Kumar surname: Singh fullname: Singh, Sanjay Kumar organization: Department of Computer Science and Engineering, IIT (B.H.U) – sequence: 3 givenname: Ali Imam surname: Abidi fullname: Abidi, Ali Imam organization: Department of Computer Science and Engineering, IIT (B.H.U) – sequence: 4 givenname: Deepanwita surname: Datta fullname: Datta, Deepanwita organization: Department of Computer Science and Engineering, IIT (B.H.U) – sequence: 5 givenname: Arun Kumar surname: Sangaiah fullname: Sangaiah, Arun Kumar email: arunkumarsangaiah@gmail.com organization: School of Computing Science and Engineering, VIT University
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Keywords	Texture descriptor Multi-modal SRC Cattle recognition Animal biometrics Fusion Muzzle point pattern
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SubjectTerms	Animals Biometrics Cattle Computer Science Computer vision Face recognition Feature extraction Feature recognition Image classification Machine learning Object recognition Processor Architectures Regression models Representations Software Engineering/Programming and Operating Systems Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm Theory of Computation
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Title	Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images
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