2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors

Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature...

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Published in:	Multimedia tools and applications Vol. 80; no. 12; pp. 18839 - 18857
Main Authors:	Bansal, Monika, Kumar, Munish, Kumar, Manish
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2021 Springer Nature B.V
Subjects:	Algorithms Classification Cluster analysis Clustering Comparative analysis Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Decision trees Feature extraction Feature recognition Image processing Multimedia Information Systems Object recognition Special Purpose and Application-Based Systems Two dimensional analysis Two dimensional models Vector quantization K-means SURF SIFT LPP ORB PCA
ISSN:	1380-7501, 1573-7721
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Abstract	Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.
AbstractList	Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.
Author	Bansal, Monika Kumar, Munish Kumar, Manish
Author_xml	– sequence: 1 givenname: Monika surname: Bansal fullname: Bansal, Monika organization: Department of Computer Science, Punjabi University – sequence: 2 givenname: Munish orcidid: 0000-0003-0115-1620 surname: Kumar fullname: Kumar, Munish email: munishcse@gmail.com organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 3 givenname: Manish surname: Kumar fullname: Kumar, Manish organization: Department of Computer Science, Baba Farid College
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Snippet	Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper...
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SubjectTerms	Algorithms Classification Cluster analysis Clustering Comparative analysis Computer Communication Networks Computer Science Computer vision Data Structures and Information Theory Decision trees Feature extraction Feature recognition Image processing Multimedia Information Systems Object recognition Special Purpose and Application-Based Systems Two dimensional analysis Two dimensional models Vector quantization
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Title	2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors
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