<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this study, we propose a geometric feature set for 2D shape retrieval. Conventional Hough feature gives the edge locations along with angle and creates Hough table if there are multiple intersections at borders. In this paper, a statistical way to represent the relation of repeating contours at each angle around the shape centroid is presented. The main contribution of this paper is to use the standard deviation of repeating contours. We calculate the angle between the shape centroid and each point on the contour. For each integer angle value, three features were extracted: the number of contour repetitions, the average distance of the points at that angle to the centroid, and the standard deviation of the points at the same angle. Thus, a 2D image was represented by a constant sized matrix, regardless of its size. In the case of similarity between two images, instead of merging features within a single expression, the algorithm picked the feature with the highest similarity rate for that comparison. We tested the proposed method on MPEG-7, Kimia99, ETH-80 datasets for a benchmark with the state-of-the-art. It outperformed most of the recent methods in terms of retrieval rate.</p>
</abstract>

In this study, we propose a geometric feature set for 2D shape retrieval. Conventional Hough feature gives the edge locations along with angle and creates Hough table if there are multiple intersections at borders. In this paper, a statistical way to represent the relation of repeating contours at each angle around the shape centroid is presented. The main contribution of this paper is to use the standard deviation of repeating contours. We calculate the angle between the shape centroid and each point on the contour. For each integer angle value, three features were extracted: the number of contour repetitions, the average distance of the points at that angle to the centroid, and the standard deviation of the points at the same angle. Thus, a 2D image was represented by a constant sized matrix, regardless of its size. In the case of similarity between two images, instead of merging features within a single expression, the algorithm picked the feature with the highest similarity rate for that comparison. We tested the proposed method on MPEG-7, Kimia99, ETH-80 datasets for a benchmark with the state-of-the-art. It outperformed most of the recent methods in terms of retrieval rate.

Shape retrieval using angle-wise contour variance

Department of Electrical and Electronics Engineering

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Shape retrieval using angle-wise contour variance"}

In this study, we propose a geometric feature set for 2D shape retrieval. Conventional Hough feature gives the edge locations along with angle and creates...

Shape retrieval using angle-wise contour variance

Published Online: May 12, 2021

Page range: 99 - 105

Received: Mar 29, 2021

DOI: https://doi.org/10.2478/jee-2021-0013

Keywords
computer vision, pattern recognition, pattern matching, image matching, image recognition

© 2021 Mustafa Eren Yildirim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Shape retrieval using angle-wise contour variance

Published Online: May 12, 2021

Page range: 99 - 105

Received: Mar 29, 2021

DOI: https://doi.org/10.2478/jee-2021-0013

Keywordscomputer vision, pattern recognition, pattern matching, image matching, image recognition

© 2021 Mustafa Eren Yildirim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
computer vision, pattern recognition, pattern matching, image matching, image recognition