Technical Report CIS-2003-02

Title: Attention-based Dynamic Visual search using Inner Scene Similarity: Algorithms and Bounds
Authors: Tamar Avraham and Michael Lindenbaum
PDF - RevisedCIS-2003-02.revised.pdf
Abstract: Visual search is required when applying a recognition process on a scene containing multiple objects. In such a case, we would like to avoid an exhaustive sequential search. This work proposes a dynamic visual search framework based mainly on inner-scene similarity. 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. We use this assumption for determining the order of attention. Both deterministic and stochastic approaches, relying on this hypothesis, are considered. Under the deterministic approach, we suggest a measure similar to Kolmogorov's $\epsilon$-covering that quantifies the difficulty of a search task. We show that this measure bounds the performance of all search algorithms and suggest a simple algorithm that meets this bound. Under the stochastic approach, we model the identity of the candidates as a set of correlated random variables and derive a search procedure based on linear estimation. Several experiments were conducted where the statistical characteristics, the search algorithm, and the bound are evaluated and verified.
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