Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features

Oleksii Gorokhovatskyi, Volodymyr Gorokhovatskyi and Olena Peredrii
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Oleksii Gorokhovatskyi: Department of Informatics and Computer Technologies, Simon Kuznets Kharkiv National University of Economics, Nauky ave. 9-A, 61166 Kharkiv, Ukraine
Volodymyr Gorokhovatskyi: Department of Informatics, Kharkiv National University of Radio Electronics, Nauky ave. 14, 61166 Kharkiv, Ukraine
Olena Peredrii: Department of Informatics and Computer Technologies, Simon Kuznets Kharkiv National University of Economics, Nauky ave. 9-A, 61166 Kharkiv, Ukraine

Data, 2018, vol. 3, issue 4, 1-10

Abstract: In this paper, we propose an investigation of the properties of structural image recognition methods in the cluster space of characteristic features. Recognition, which is based on key point descriptors like SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), etc., often relating to the search for corresponding descriptor values between an input image and all etalon images, which require many operations and time. Recognition of the previously quantized (clustered) sets of descriptor features is described. Clustering is performed across the complete set of etalon image descriptors and followed by screening, which allows for representation of each etalon image in vector form as a distribution of clusters. Due to such representations, the number of computation and comparison procedures, which are the core of the recognition process, might be reduced tens of times. Respectively, the preprocessing stage takes additional time for clustering. The implementation of the proposed approach was tested on the Leeds Butterfly dataset. The dependence of cluster amount on recognition performance and processing time was investigated. It was proven that recognition may be performed up to nine times faster with only a moderate decrease in quality recognition compared to searching for correspondences between all existing descriptors in etalon images and input one without quantization.

Keywords: computer vision; structural recognition methods; set of characteristic features; descriptor; quantization; clustering; competitive learning; recognition performance; recognition accuracy (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
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