A Steiner Tree Approach to Object Detection

We propose an approach to speeding up object detection, with an emphasis on settings where multiple object classes are being detected. Our method uses a segmentation algorithm to select a small number of image regions on which to run a classifier. Compared to the classical sliding window approach, this results in a significantly smaller number of rectangles examined, and thus significantly faster object detection. Further, in the multiple object class setting, we show that the computational cost of proposing candidate regions can be amortized across objects classes, resulting in an additional speedup. At the heart of our approach is a reduction to a directed Steiner tree optimization problem, which we solve approximately in order to select the segmentation algorithm parameters. The solution gives a small set of segmentation strategies that can be shared across object classes. Compared to the sliding window approach, our method results in two orders of magnitude fewer regions considered, and significant (10-15x) running time speedups on challenging object detection datasets (LabelMe and StreetScenes) while maintaining comparable detection accuracy. Authors: Olga Russakovsky, Andrew Y. Ng (2010)
AUTHORED BY
Olga Russakovsky
Andrew Y. Ng

Abstract

We propose an approach to speeding up object detection, with an emphasis on settings where multiple object classes are being detected. Our method uses a segmentation algorithm to select a small number of image regions on which to run a classifier. Compared to the classical sliding window approach, this results in a significantly smaller number of rectangles examined, and thus significantly faster object detection. Further, in the multiple object class setting, we show that the computational cost of proposing candidate regions can be amortized across objects classes, resulting in an additional speedup. At the heart of our approach is a reduction to a directed Steiner tree optimization problem, which we solve approximately in order to select the segmentation algorithm parameters. The solution gives a small set of segmentation strategies that can be shared across object classes. Compared to the sliding window approach, our method results in two orders of magnitude fewer regions considered, and significant (10-15x) running time speedups on challenging object detection datasets (LabelMe and StreetScenes) while maintaining comparable detection accuracy.

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