Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval
Abstract Shape recognition and retrieval is a complex task on non‐rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-04-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12088 |
| _version_ | 1811266449940938752 |
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| author | Arjun Paramarthalingam Mirnalinee Thankanadar |
| author_facet | Arjun Paramarthalingam Mirnalinee Thankanadar |
| author_sort | Arjun Paramarthalingam |
| collection | DOAJ |
| description | Abstract Shape recognition and retrieval is a complex task on non‐rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi‐triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG‐7 Part‐A, Part‐B and multi‐view curve dataset images. The present experiments are aimed to check proposed shape descriptor's robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors. |
| first_indexed | 2024-04-12T20:43:28Z |
| format | Article |
| id | doaj.art-276c334938494d97b6d966b13e2375e5 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-12T20:43:28Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-276c334938494d97b6d966b13e2375e52022-12-22T03:17:21ZengWileyIET Image Processing1751-96591751-96672021-04-011551093110410.1049/ipr2.12088Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrievalArjun Paramarthalingam0Mirnalinee Thankanadar1Assistant Professor, Computer Science and Engineering University College of Engineering Villupuram Tamilnadu IndiaProfessor, Computer Science and Engineering SSN College of Engineering Kalavakkam Tamilnadu IndiaAbstract Shape recognition and retrieval is a complex task on non‐rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi‐triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG‐7 Part‐A, Part‐B and multi‐view curve dataset images. The present experiments are aimed to check proposed shape descriptor's robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors.https://doi.org/10.1049/ipr2.12088AlgebraImage recognitionComputer vision and image processing techniquesCombinatorial mathematicsAlgebraCombinatorial mathematics |
| spellingShingle | Arjun Paramarthalingam Mirnalinee Thankanadar Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval IET Image Processing Algebra Image recognition Computer vision and image processing techniques Combinatorial mathematics Algebra Combinatorial mathematics |
| title | Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval |
| title_full | Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval |
| title_fullStr | Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval |
| title_full_unstemmed | Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval |
| title_short | Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval |
| title_sort | extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval |
| topic | Algebra Image recognition Computer vision and image processing techniques Combinatorial mathematics Algebra Combinatorial mathematics |
| url | https://doi.org/10.1049/ipr2.12088 |
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