A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval
Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same en...
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| Format: | Article |
| Language: | English |
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Korean Society for Internet Information
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/47467/1/A%20Salient%20Based%20Bag%20of%20Visual%20Word%20model%20%28SBBoVW%29%20improvements%20toward%20difficult%20object%20recognition%20and%20object%20location%20in%20image%20retrieval.pdf |
| _version_ | 1825929811378307072 |
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| author | Mansourian, Leila Abdullah, Muhamad Taufik Abdullah, Lilli Nurliyana Azman, Azreen Mustaffa, Mas Rina |
| author_facet | Mansourian, Leila Abdullah, Muhamad Taufik Abdullah, Lilli Nurliyana Azman, Azreen Mustaffa, Mas Rina |
| author_sort | Mansourian, Leila |
| collection | UPM |
| description | Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts. |
| first_indexed | 2024-03-06T09:02:10Z |
| format | Article |
| id | upm.eprints-47467 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:02:10Z |
| publishDate | 2016 |
| publisher | Korean Society for Internet Information |
| record_format | dspace |
| spelling | upm.eprints-474672016-05-20T01:44:51Z http://psasir.upm.edu.my/id/eprint/47467/ A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval Mansourian, Leila Abdullah, Muhamad Taufik Abdullah, Lilli Nurliyana Azman, Azreen Mustaffa, Mas Rina Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts. Korean Society for Internet Information 2016 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47467/1/A%20Salient%20Based%20Bag%20of%20Visual%20Word%20model%20%28SBBoVW%29%20improvements%20toward%20difficult%20object%20recognition%20and%20object%20location%20in%20image%20retrieval.pdf Mansourian, Leila and Abdullah, Muhamad Taufik and Abdullah, Lilli Nurliyana and Azman, Azreen and Mustaffa, Mas Rina (2016) A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval. KSII Transactions on Internet and Information Systems, 10 (2). pp. 769-786. ISSN 1976-7277 http://www.itiis.org/digital-library/manuscript/1257 10.3837/tiis.2016.02.018 |
| spellingShingle | Mansourian, Leila Abdullah, Muhamad Taufik Abdullah, Lilli Nurliyana Azman, Azreen Mustaffa, Mas Rina A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval |
| title | A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval |
| title_full | A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval |
| title_fullStr | A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval |
| title_full_unstemmed | A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval |
| title_short | A Salient Based Bag of Visual Word model (SBBoVW): improvements toward difficult object recognition and object location in image retrieval |
| title_sort | salient based bag of visual word model sbbovw improvements toward difficult object recognition and object location in image retrieval |
| url | http://psasir.upm.edu.my/id/eprint/47467/1/A%20Salient%20Based%20Bag%20of%20Visual%20Word%20model%20%28SBBoVW%29%20improvements%20toward%20difficult%20object%20recognition%20and%20object%20location%20in%20image%20retrieval.pdf |
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