TY - GEN
T1 - Adjustment learning and relevant component analysis
AU - Shental, Noam
AU - Hertz, Tomer
AU - Weinshall, Daphna
AU - Pavel, Misha
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002
Y1 - 2002
N2 - We propose a new learning approach for image retrieval, which we call adjustment learning, and demonstrate its use for face recognition and color matching. Our approach is motivated by a frequently encountered problem, namely, that variability in the original data representation which is not relevant to the task may interfere with retrieval and make it very difficult. Our key observation is that in real applications of image retrieval, data sometimes comes in small chunks - small subsets of images that come from the same (but unknown) class. This is the case, for example, when a query is presented via a short video clip. We call these groups chunklets, and we call the paradigm which uses chunklets for unsupervised learning adjustment learning. Within this paradigm we propose a linear scheme, which we call Relevant Component Analysis; this scheme uses the information in such chunklets to reduce irrelevant variability in the data while amplifying relevant variability. We provide results using our method on two problems: face recognition (using a database publicly available on the web), and visual surveillance (using our own data). In the latter application chunklets are obtained automatically from the data without the need of supervision.
AB - We propose a new learning approach for image retrieval, which we call adjustment learning, and demonstrate its use for face recognition and color matching. Our approach is motivated by a frequently encountered problem, namely, that variability in the original data representation which is not relevant to the task may interfere with retrieval and make it very difficult. Our key observation is that in real applications of image retrieval, data sometimes comes in small chunks - small subsets of images that come from the same (but unknown) class. This is the case, for example, when a query is presented via a short video clip. We call these groups chunklets, and we call the paradigm which uses chunklets for unsupervised learning adjustment learning. Within this paradigm we propose a linear scheme, which we call Relevant Component Analysis; this scheme uses the information in such chunklets to reduce irrelevant variability in the data while amplifying relevant variability. We provide results using our method on two problems: face recognition (using a database publicly available on the web), and visual surveillance (using our own data). In the latter application chunklets are obtained automatically from the data without the need of supervision.
UR - http://www.scopus.com/inward/record.url?scp=84937544784&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937544784&partnerID=8YFLogxK
U2 - 10.1007/3-540-47979-1_52
DO - 10.1007/3-540-47979-1_52
M3 - Conference contribution
AN - SCOPUS:84937544784
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 776
EP - 790
BT - Computer Vision - ECCV 2002 - 7th European Conference on Computer Vision, Proceedings
A2 - Heyden, Anders
A2 - Sparr, Gunnar
A2 - Nielsen, Mads
A2 - Johansen, Peter
PB - Springer-Verlag
T2 - 7th European Conference on Computer Vision, ECCV 2002
Y2 - 28 May 2002 through 31 May 2002
ER -