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Cattle identification system: a comparative analysis of SIFT, SURF and ORB feature descriptors

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Abstract

Image processing is a key research area in computer vision that recognizes images and assigns labels to the extracted features. This new paradigm has recently received significant attention in biometric features that aid in the identification of breed and individual cattle based on extracted muzzle point features. The primary goal of this article is to present a comparison of various feature descriptors and classifiers for cattle identification using muzzle points. This paper also involves identification based on various features extracted from an image. There are a few feature extraction techniques available, such as Holistic features extraction, ORB, SIFT, SURF, Shi-Tomasi, Harris corner detection, and so on. The authors of this article considered three feature descriptor algorithms, SIFT (Scale Invariant Feature Transform), SURF (Speeded up Robust Feature), and ORB (Oriented Fast and Rotated BRIEF), to carry out experimental work on cattle images for breed identification system. A differentiation among three descriptors is presented in this article by determining them individually and in a combination of these methodologies. Classifiers such as Decision Tree, k-NN, and Random Forest are used to categorize images based on extracted features. The experiments are carried out on a dataset of four breeds: Holstein Friesian (470 images), Jersey (200 images), Rathi (100 images), and Sahiwal (100 images) (160 images). In the partitioning strategy, 80% of the data is considered as training dataset, and the remaining 20% is considered a testing set. The accuracy of 97.23% is achieved by hybrid feature techniques composed of SIFT, SURF, and ORB for the cattle identification system. This paper describes the feature extraction and classifiers used in the cattle identification system, data collection, methodology, and design of the proposed system, evaluates results, and represents the system’s relevance and future prospects.

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Authors and Affiliations

  1. Department of Computer Science and Applications, Guru Nanak College, Sri Muktsar Sahib, Chennai, Punjab, India

    Amanpreet Kaur

  2. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

  3. Department of Computer Science and Applications, Panjab University Regional Centre, Sri Muktsar Sahib, Chandigarh, Punjab, India

    M. K. Jindal

Authors
  1. Amanpreet Kaur
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  2. Munish Kumar
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  3. M. K. Jindal
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Contributions

Amanpreet Kaur: Writing - original draft, Conceptualization, Methodology, Implementation.

Munish Kumar: Experimental Work, Testing, Writing - review & editing.

Manish Kumar Jindal: Review & editing final draft.

Corresponding author

Correspondence to Munish Kumar.

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The authors have created their own dataset for performing the experiments in the considered work.

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Kaur, A., Kumar, M. & Jindal, M.K. Cattle identification system: a comparative analysis of SIFT, SURF and ORB feature descriptors. Multimed Tools Appl 82, 27391–27413 (2023). https://doi.org/10.1007/s11042-023-14478-y

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  • DOI: https://doi.org/10.1007/s11042-023-14478-y

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