Colloquium Speaker

Dr. Michael Lindenbaum, Computer Science Department
Technion, Israel Institute of Technology
Dynamic Visual Search Using Inner-Scene Similarity: Algorithms and Inherent Limitations
Date: September 24, 2004
Time: 2:00 PM
Place: Gould-Simpson, Room 701
Refreshments will be served in the 7th floor lobby of Gould-Simpson at 1:45 PM


A dynamic visual search framework based mainly on inner-scene similarity is proposed. Algorithms as well as measures quantifying the difficulty of search tasks are suggested.

Given a number of candidates (e.g. sub-images), our basic hypothesis is that more visually similar candidates are more likely to have the same identity. Both deterministic and stochastic approaches, relying on this hypothesis, are used to quantify this intuition.

Under the deterministic approach, we suggest a measure similar to Kolmogorov's $\epsilon$-covering that quantifies the difficulty of a search task and bounds the performance of all search algorithms. We also suggest a simple algorithm that meets this bound.

Under the stochastic approach, we model the identities of the candidates as correlated random variables and characterize the task using its second order statistics. We derive a search procedure based on minimum MSE linear estimation. Simple extensions enable the algorithm to use top-down and/or bottom-up information, when available. Both approaches are evaluated experimentally.

A joint work with Tamar Avraham.