TY - GEN
T1 - Learning feature relevance and similarity metrics in image databases
AU - Bhanu, B.
AU - Peng, Jing
AU - Qing, Shan
N1 - Publisher Copyright:
© 1998 IEEE.
PY - 1998
Y1 - 1998
N2 - Most of the current image retrieval systems use »one-shot» queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithm is used where the weights of the features that are used to represent images remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, neither all of the features are equally important for a given query nor a similarity metric is optimal for all kinds of images in a database. The manual adjustment of these weights and the selection of similarity metric are exhausting. Moreover, they require a very sophisticated user. The authors present a novel image retrieval system that continuously learns the weights of features and selects an appropriate similarity metric based on the user's feedback given as positive or negative image examples. Experimental results are presented that provide the objective evaluation of learning behavior of the system for image retrieval.
AB - Most of the current image retrieval systems use »one-shot» queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithm is used where the weights of the features that are used to represent images remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, neither all of the features are equally important for a given query nor a similarity metric is optimal for all kinds of images in a database. The manual adjustment of these weights and the selection of similarity metric are exhausting. Moreover, they require a very sophisticated user. The authors present a novel image retrieval system that continuously learns the weights of features and selects an appropriate similarity metric based on the user's feedback given as positive or negative image examples. Experimental results are presented that provide the objective evaluation of learning behavior of the system for image retrieval.
UR - http://www.scopus.com/inward/record.url?scp=84901404814&partnerID=8YFLogxK
U2 - 10.1109/IVL.1998.694471
DO - 10.1109/IVL.1998.694471
M3 - Conference contribution
AN - SCOPUS:84901404814
SN - 0818685441
SN - 9780818685446
T3 - Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1998
SP - 14
EP - 18
BT - Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1998
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1998 IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 1998
Y2 - 21 June 1998
ER -