Image Representations Beyond Histograms of Gradients: The Role of Gestalt Descriptors

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 and Dalan and Triggs' histogram of oriented gradients feature. Given that both of these baseline features have already shown state of the art performance in multiple object detection benchmarks, that our new mid-level representations can further improve detection results warrants special consideration. We evaluate the performance of these detection systems on the publicly available StreetScenes and Caltech101 databases among others.