Efficiently Combining Contour and Texture Cues for Object Recognition

This paper proposes an efficient fusion of contour and texture cues for image categorization and object detection. Our work confirms and strengthens recent results that combining complementary feature types improves performance. We obtain a similar improvement in accuracy and additionally an improvement in efficiency. We use a boosting algorithm to learn models that use contour and texture features. Our main contributions are (i) the use of dense generic texture features to complement contour fragments, and (ii) a simple feature selection mechanism that includes the computational costs of features in order to learn a run-time efficient model. Our evaluation on 17 challenging and varied object classes confirms that the synergy of the two feature types performs significantly better than either alone, and that computational efficiency is substantially improved using our feature selection mechanism. An investigation of the boosted features shows a fascinating emergent property: the absence of certain textures often contributes towards object detection. Comparison with recent work shows that performance is state of the art.