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Feng-Cheng CHANG, Hsueh-Ming HANG, "A Relevance Feedback Image Retrieval Scheme Using Multi-Instance and Pseudo Image Concepts" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 5, pp. 1720-1731, May 2006, doi:10.1093/ietisy/e89-d.5.1720.
Abstract:Content-based image search has long been considered a difficult task. Making correct conjectures on the user intention (perception) based on the query images is a critical step in the content-based search. One key concept in this paper is how we find the user preferred low-level image characteristics from the multiple positive samples provided by the user. The second key concept is how we generate a set of consistent "pseudo images" when the user does not provide a sufficient number of samples. The notion of image feature stability is thus introduced. The third key concept is how we use negative images as pruning criterion. In realizing the preceding concepts, an image search scheme is developed using the weighted low-level image features. At the end, quantitative simulation results are used to show the effectiveness of these concepts.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.5.1720/_p

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@ARTICLE{e89-d_5_1720,
author={Feng-Cheng CHANG, Hsueh-Ming HANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Relevance Feedback Image Retrieval Scheme Using Multi-Instance and Pseudo Image Concepts},
year={2006},
volume={E89-D},
number={5},
pages={1720-1731},
abstract={Content-based image search has long been considered a difficult task. Making correct conjectures on the user intention (perception) based on the query images is a critical step in the content-based search. One key concept in this paper is how we find the user preferred low-level image characteristics from the multiple positive samples provided by the user. The second key concept is how we generate a set of consistent "pseudo images" when the user does not provide a sufficient number of samples. The notion of image feature stability is thus introduced. The third key concept is how we use negative images as pruning criterion. In realizing the preceding concepts, an image search scheme is developed using the weighted low-level image features. At the end, quantitative simulation results are used to show the effectiveness of these concepts.},
keywords={},
doi={10.1093/ietisy/e89-d.5.1720},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - A Relevance Feedback Image Retrieval Scheme Using Multi-Instance and Pseudo Image Concepts
T2 - IEICE TRANSACTIONS on Information
SP - 1720
EP - 1731
AU - Feng-Cheng CHANG
AU - Hsueh-Ming HANG
PY - 2006
DO -10.1093/ietisy/e89-d.5.1720
JO - IEICE TRANSACTIONS on Information
SN -1745-1361
VL - E89-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2006
AB -Content-based image search has long been considered a difficult task. Making correct conjectures on the user intention (perception) based on the query images is a critical step in the content-based search. One key concept in this paper is how we find the user preferred low-level image characteristics from the multiple positive samples provided by the user. The second key concept is how we generate a set of consistent "pseudo images" when the user does not provide a sufficient number of samples. The notion of image feature stability is thus introduced. The third key concept is how we use negative images as pruning criterion. In realizing the preceding concepts, an image search scheme is developed using the weighted low-level image features. At the end, quantitative simulation results are used to show the effectiveness of these concepts.
ER -