Abstract
Detecting and recognizing objects in images is one of the most challenging tasks in computer vision, as it seeks to detect subtle objects while ignoring massive numbers of negatives. While deep networks have led to advances in many problems, new representations and approaches are needed for applications without millions of training samples or where explanations are required. This paper focuses on a new representation that can be used for detection/recognition in many applications of computer vision, and demonstrates it on two very different applications: pedestrian detection and ear recognition. This paper proposes the use of Chainlets, ordered oriented data computed from deep contour-based edge detection, as a novel object descriptor. Chainlets address the problem with Histograms of Oriented Gradients, in that HOG does not model edge connectedness. We extend HOG using Histograms of Chain Codes, which improve object descriptiveness and can even provide orientation invariance. These descriptors significantly outperform existing feature sets, including both existing hand-crafted and deep features for human ear recognition, and are near state of the art on pedestrian detection. Results from our Chainlets algorithm underwent independent testing as part of the new Unconstrained Ear Recognition Challenge dataset, where the competition's evaluation showed Chainlets yielded a significant improvement over other state of the art approaches. To show further generality, we performed an evaluation on the INRIA person detection dataset with results that are near state-of-the-art deep network and boosted classifier results. Overall, the experimental results show that the novel Chainlets representation is competitive with, or better than, state-of-the-art algorithms on both pedestrian detection and ear recognition applications.