Abstract:
Histograms of orientations and the statistics derived from them have
proven to be effective image representations for various recognition
tasks. In this work we attempt to improve the accuracy of object detection
systems by including new features that explicitly capture mid-level
gestalt concepts. Four new image features are proposed, inspired by the
gestalt principles of continuity, symmetry, closure and repetition. The
resulting image representations are used jointly with existing
state-of-the-art features and together enable better detectors for
challenging real-world data sets. As baseline features, we use Riesenhuber
and Poggio's C1 features [15] and Dalan and Triggs' Histogram of Oriented
Gradients feature [6]. Given that both of these baseline features have
already shown state of the art performance in multiple object detection
benchmarks, that our new midlevel representations can further improve
detection results warrants special consideration. We evaluate the
performance of these detection systems on the publicly available
StreetScenes [25] and Caltech101 [11] databases among others.
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