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

<p>Shape representation and retrieval are essential research topics of computer vision. This paper proposes a novel feature set to be used in content-based image retrieval systems. The proposed method is an extended version of our previous study which uses contour information of shapes. The previous study calculated the center of mass (CoM) of the shape. By taking the CoM as origin, we created imaginary vectors in every angular direction. From each vector, we extracted three features which are the number of intersections between vector and contour, average distance of intersection points to CoM, and standard deviation of these points. In this method, we extract novel features and decrease the size of the feature set to decrease the computation time. We divide the shape into quadrants and represent each quadrant by nine features. Each shape image is represented by a 4x9 feature vector. We tested the proposed method on MPEG-7 and ETH-80 datasets and compared it with the state-of-art. According to the results, our method decreased the computation time dramatically while giving a state-of-art level retrieval accuracy.</p>
</abstract>

Shape representation and retrieval are essential research topics of computer vision. This paper proposes a novel feature set to be used in content-based image retrieval systems. The proposed method is an extended version of our previous study which uses contour information of shapes. The previous study calculated the center of mass (CoM) of the shape. By taking the CoM as origin, we created imaginary vectors in every angular direction. From each vector, we extracted three features which are the number of intersections between vector and contour, average distance of intersection points to CoM, and standard deviation of these points. In this method, we extract novel features and decrease the size of the feature set to decrease the computation time. We divide the shape into quadrants and represent each quadrant by nine features. Each shape image is represented by a 4x9 feature vector. We tested the proposed method on MPEG-7 and ETH-80 datasets and compared it with the state-of-art. According to the results, our method decreased the computation time dramatically while giving a state-of-art level retrieval accuracy.

Quadrant-based contour features for accelerated shape retrieval system

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":"Quadrant-based contour features for accelerated shape retrieval system"}

Shape representation and retrieval are essential research topics of computer vision. This paper proposes a novel feature set to be used in content-based image...

Quadrant-based contour features for accelerated shape retrieval system

Publié en ligne: 11 juil. 2022

Pages: 197 - 202

Reçu: 02 juin 2022

DOI: https://doi.org/10.2478/jee-2022-0026

Mots clés
image retrieval, shape representation, contour-based feature

© 2022 Mustafa Eren Yildirim, published by Sciendo

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

Quadrant-based contour features for accelerated shape retrieval system

Publié en ligne: 11 juil. 2022

Pages: 197 - 202

Reçu: 02 juin 2022

DOI: https://doi.org/10.2478/jee-2022-0026

Mots clésimage retrieval, shape representation, contour-based feature

© 2022 Mustafa Eren Yildirim, published by Sciendo

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

Mots clés
image retrieval, shape representation, contour-based feature