A novel image retrieval technique based on semi supervised clustering
Traditionally Content-Based Image Retrieval (CBIR) problems investigate the occurrence of images matching to a user-submitted query image or a sketch drawn by the user within a large image collection. However, there is often limited support for retrieving semantically similar images from large datab...
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| Vydáno v: | Multimedia tools and applications Ročník 80; číslo 28-29; s. 35741 - 35769 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.11.2021
Springer Nature B.V |
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| ISSN: | 1380-7501, 1573-7721 |
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| Abstract | Traditionally Content-Based Image Retrieval (CBIR) problems investigate the occurrence of images matching to a user-submitted query image or a sketch drawn by the user within a large image collection. However, there is often limited support for retrieving semantically similar images from large databases, matching the user’s perception. In this paper, we try to address this semantic gap problem in CBIR by performing a clustering-based retrieval. In the proposed approach we first perform a continuous probabilistic semi-supervised clustering to group similar images to form macro clusters. Macro clusters so formed, ensures class-wise similarity instead of semantic similarity. To retrieve the semantically matching images from these macro clusters formed, the CBIR method is adopted using a cluster within-cluster approach. The key idea is that the macro clusters formed during the initial phase of classification are further classified into micro clusters based on the decision tree approach. For retrieval, as the first step, the macro cluster matching to the user’s query is found. In the next step, to ensure semantic similarity the image is classified to the matching micro cluster. The proposed method is experimentally evaluated first on Wang database which contains complex and diverse images with varying fine details. Further, the experiments are repeated on the Ponce group database and Corel 5K database. The experimental results obtained demonstrate the effectiveness of the proposed approach. |
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| AbstractList | Traditionally Content-Based Image Retrieval (CBIR) problems investigate the occurrence of images matching to a user-submitted query image or a sketch drawn by the user within a large image collection. However, there is often limited support for retrieving semantically similar images from large databases, matching the user’s perception. In this paper, we try to address this semantic gap problem in CBIR by performing a clustering-based retrieval. In the proposed approach we first perform a continuous probabilistic semi-supervised clustering to group similar images to form macro clusters. Macro clusters so formed, ensures class-wise similarity instead of semantic similarity. To retrieve the semantically matching images from these macro clusters formed, the CBIR method is adopted using a cluster within-cluster approach. The key idea is that the macro clusters formed during the initial phase of classification are further classified into micro clusters based on the decision tree approach. For retrieval, as the first step, the macro cluster matching to the user’s query is found. In the next step, to ensure semantic similarity the image is classified to the matching micro cluster. The proposed method is experimentally evaluated first on Wang database which contains complex and diverse images with varying fine details. Further, the experiments are repeated on the Ponce group database and Corel 5K database. The experimental results obtained demonstrate the effectiveness of the proposed approach. |
| Author | S, Nisha Chandran Gangodkar, Durgaprasad |
| Author_xml | – sequence: 1 givenname: Nisha Chandran orcidid: 0000-0002-0558-5496 surname: S fullname: S, Nisha Chandran email: nisha.unnikrishnan@gmail.com organization: School of Computing, Graphic Era Hill University – sequence: 2 givenname: Durgaprasad surname: Gangodkar fullname: Gangodkar, Durgaprasad organization: Dept. of CSE, Graphic Era University |
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| Cites_doi | 10.1007/3-540-45113-7_21 10.1109/34.895972 10.1155/2019/9658350 10.1109/83.817596 10.1145/244130.244151 10.1109/TIP.2009.2035882 10.1109/69.755615 10.1109/ICASSP.2005.1416495 10.1109/TKDE.2002.1033769 10.1016/j.patcog.2007.12.003 10.1142/S0219622009003363 10.1142/S0219622011004634 10.1007/s10844-013-0257-4 10.1145/973264.973295 10.1007/BF00123143 10.1007/s12517-014-1584-7 10.1016/j.knosys.2014.10.009 10.1142/S0219622007002496 10.1007/s11042-019-7321-1 10.1007/s11042-017-4664-3 10.1016/j.ijleo.2015.05.002 10.1007/0-387-25465-X_15 10.1145/354756.354775 10.1109/2.410146 10.1109/76.718510 10.1007/3-540-45783-6_20 10.1016/j.jvcir.2016.03.008 10.1109/TIP.2008.2007385 10.1109/DICTA.2012.6411665 10.1145/2647868.2654948 10.1016/j.ins.2013.08.043 10.1109/TPAMI.2002.1023800 10.1109/ACV.1996.572008 10.1109/34.955109 10.1016/j.cviu.2005.09.012 10.1016/S0262-8856(96)01114-6 |
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| DOI | 10.1007/s11042-021-11542-3 |
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