An efficient technique for object recognition using Shi-Tomasi corner detection algorithm
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METHODOLOGIES AND APPLICATION
An efficient technique for object recognition using Shi-Tomasi corner detection algorithm Monika Bansal1 • Munish Kumar2 • Manish Kumar3 • Krishan Kumar4
Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract An efficient feature detection algorithm and image classification is a very crucial task in computer vision system. There are various state-of-the-art feature detectors and descriptors available for an object recognition task. In this paper, the authors have compared the performance of Shi-Tomasi corner detector with SIFT and SURF feature descriptors and evaluate the performance of Shi-Tomasi in combination with SIFT and SURF feature descriptors. To make the computations faster, authors have reduced the size of features computed in all cases by applying locality preserving projection methodology. Features extracted using these algorithms are further classified with various classifiers like K-NN, decision tree and random forest. For experimental work, a public dataset, namely Caltech-101 image dataset, is considered in this paper. This dataset comprises of 101 object classes. These classes have further contained many images. Using a combination of Shi-Tomasi, SIFT and SURF features, the authors have achieved a recognition accuracy of 85.9%, 80.8% and 74.8% with random forest, decision tree and K-NN classifier, respectively. In this paper, the authors have also computed true positive rate, false positive rate and area under curve in all cases. Finally, the authors have applied the adaptive boosting methodology to improve the recognition accuracy. Authors have reported improved recognition accuracy of 86.4% using adaptive boosting with random forest classifier and a combination of Shi-Tomasi, SIFT and SURF features. Keywords Decision tree LPP k-NN Random forest Shi-Tomasi SIFT SURF
1 Introduction Nowadays, object recognition is a hot research area in the domain of image processing and computer vision where an object is recognized from an image. This system works just like a child learned in school. A child is trained in school by learning various shapes and object names. When he learned all objects, he can determine all similar objects that
Communicated by V. Loia. & Munish Kumar [email protected] 1
Department of Computer Science, Punjabi University, Patiala, India
2
Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India
3
Department of Computer Science, Baba Farid College, Bathinda, Punjab, India
4
University Institute of Engineering and Technology, Panjab University, Chandigarh, India
he has already learned. The machine must be fully trained with various machine learning algorithms by adding all the object names just like a child learn. The training process is done by storing all the features of similar objects in a database. Then, an input image is to be tested by matching the features of the input image with the stored feature dataset of
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