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2D Shape Recognition and Retrieval Using Shape Contour Based on the 8-Neighborhood Patterns Matching Technique

2D Shape Recognition and Retrieval Using Shape Contour Based on the 8-Neighborhood Patterns Matching Technique

Muzameel Ahmed, Manjunath Aradhya
Copyright: © 2019 |Volume: 10 |Issue: 2 |Pages: 13
ISSN: 1947-9093|EISSN: 1947-9107|EISBN13: 9781522566212|DOI: 10.4018/IJSE.2019070104
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MLA

Ahmed, Muzameel, and Manjunath Aradhya. "2D Shape Recognition and Retrieval Using Shape Contour Based on the 8-Neighborhood Patterns Matching Technique." IJSE vol.10, no.2 2019: pp.49-61. http://doi.org/10.4018/IJSE.2019070104

APA

Ahmed, M. & Aradhya, M. (2019). 2D Shape Recognition and Retrieval Using Shape Contour Based on the 8-Neighborhood Patterns Matching Technique. International Journal of Synthetic Emotions (IJSE), 10(2), 49-61. http://doi.org/10.4018/IJSE.2019070104

Chicago

Ahmed, Muzameel, and Manjunath Aradhya. "2D Shape Recognition and Retrieval Using Shape Contour Based on the 8-Neighborhood Patterns Matching Technique," International Journal of Synthetic Emotions (IJSE) 10, no.2: 49-61. http://doi.org/10.4018/IJSE.2019070104

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Abstract

A technique for 2D shape recognition and retrieval is proposed. The proposed technique is based on the 8-neighborhood pattern which represents each point or pixel on the contour of the shape. These patterns are used as a framework in matching the shape of the object. The recognition and retrieval process are conducted by traversing through the contour of the shape and analyzes each point on the contour by considering the 8-neighborhood pattern. The 8-neighborhood patterns are assigned unique labels which are computed on their every occurrence during contour traversal. The cost of the best match between the shapes is evaluated by comparing the hit value obtained by the contour traversal of the shapes to be matched. The recognition and retrieval are carried out using the leave-one-out strategy and standard bull eye score, respectively. The proposed method is experimented on the MPEG-7 data set and the chicken piece data set. The results both for recognition and retrieval outperform most of the previously proposed methods.

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