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With respect to my research interest on image databases, the motivation is as follows. As the number of on-line digital images has increased rapidly, the development of efficient and effective retrieval of images is necessary. Many existing image database systems support whole-image queries, which require users to specify the contents of the whole images to be retrieved. However, users may only remember or care about some, but not all, portions of the images (i.e., subimages) they have seen before. Techniques for handling subimage queries of arbitrary size are therefore in demand. Unfortunately, not many image database management systems can handle these subimage queries. Among the systems that can deal with subimage queries of arbitrary size, multiscale similarity matching is rarely used. To this end, we developed techniques based on multiscale similarity matching to handle subimage queries of arbitrary size, and applied the techniques in large image databases.
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EXPERT SYSTEMS WITH APPLICATIONS (2024): 122754
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATAno. 1 (2024): 32:1-32:34
COMPSACpp.1325-1330, (2023)
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2023 IEEE International Conference on Industrial Technology (ICIT)pp.1-6, (2023)
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IDEASpp.17-24, (2023)
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A. M. Aahad, Khondker Bin Yamin,Md Samiullah,Chowdhury Farhan Ahmed,Carson K. Leung,Evan W. R. Madill,Adam G. M. Pazdor
2023 IEEE 35TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAIpp.221-228, (2023)
AINA (1)pp.393-405, (2023)
2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)pp.1-10, (2023)
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2023 27th International Conference Information Visualisation (IV)pp.323-328, (2023)
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