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Download the Paper (939 K, PDF file) - January 1, 2006 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

ABSTRACT:
A combination of techniques that is becoming increasingly popular is the construction of part-based object represen- tations using the outputs of interest-point detectors. Our contributions in this paper are twofold: first, we propose a primal-sketch-based set of image tokens that are used for object representation and detection. Second, top-down information is introduced based on an efficient method for the evaluation of the likelihood of hypothesized part locations. This allows us to use graphical model techniques to complement bottom-up detection, by proposing and finding the parts of the object that were missed by the front-end feature detection stage. Detection results for four object categories validate the merits of this joint top-down and bottom-up approach.

SUGGESTED CITATION:
Iasonas Kokkinos, Petros Maragos, and Alan L. Yuille, "Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models" (January 1, 2006). Department of Statistics, UCLA. Department of Statistics Papers. Paper 2006010104.
http://repositories.cdlib.org/uclastat/papers/2006010104