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Analysis of Different Feature Description Algorithm in object Recognition
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Author(s): Sirshendu Hore (HETC, India), Sankhadeep Chatterjee (University of Calcutta, India), Shouvik Chakraborty (University of Kalyani, India)and Rahul Kumar Shaw (HETC, India)
Copyright: 2017
Pages: 34
Source title:
Feature Detectors and Motion Detection in Video Processing
Source Author(s)/Editor(s): Nilanjan Dey (Techno India College of Technology, Kolkata, India), Amira Ashour (Tanta University, Egypt)and Prasenjit Kr. Patra (Bengal College of Engineering and Technology, India)
DOI: 10.4018/978-1-5225-1025-3.ch004
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Abstract
Object recognition can be done based on local feature description algorithm or through global feature description algorithm. Both types of these descriptors have the efficiency in recognizing an object quickly and accurately. The proposed work judges their performance in different circumstances such as rotational effect scaling effect, illumination effect and blurring effect. Authors also investigate the speed of each algorithm in different situations. The experimental result shows that each one has some advantages as well as some drawbacks. SIFT (Scale Invariant Feature Transformation) and SURF (Speeded Up Robust Features) performs relatively better under scale and rotation change. MSER (Maximally stable extremal regions) performs better under scale change, MinEigen in affine change and illumination change while FAST (Feature from Accelerated segment test) and SURF consume less time.
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