Vision and Robotics Seminar
School of Computer Science and Engineering
The Center for Neural Computation
The Hebrew University
will speak on
Adjustment Learning and Relevant Component Analysis
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.
Joint work with Tomer Hertz and Daphna Weinshall from HUJI and Misha
Pavel from OGI.
The lecture will take place in the
Lecture Hall, Room 1, Ziskind Building
on Thursday, June 20, 2002