In the recent years, a number of novel, deep-learning based, interest point detectors, such as LIFT, DELF, Superpoint or LF-Net was proposed. However there’s a lack of a standard benchmark to evaluate suitability of these novel keypoint detectors for real-live applications such as autonomous driving. Traditional benchmarks (e.g. Oxford VGG) are rather limited, as they consist of relatively few images of mostly planar scenes taken in favourable conditions. In this paper we verify if the recent, deep-learning based interest point detectors have the advantage over the traditional, hand-crafted keypoint detectors. To this end, we evaluate stability of a number of hand crafted and recent, learning-based interest point detectors on the street-level view ApolloScape dataset.
LIFT (Learned Invariant Feature Transform) @cite_16 is the first end-to-end, deep learning-based, method including feature detection, orientation estimation and robust descriptor extraction. The learning is based on SfM-verified DoG @cite_24 keypoints detected in a large collection of images. The feature extraction pipeline is trained by presenting quadruples of image patches (base patch, positive example, negative example and a patch with no distinctive feature points) to the network having a Siamese-like architecture with four branches.
@cite_24 @cite_16
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We present an unsupervised method for learning a hierarchy of sparse feature detectors that are invariant to small shifts and distortions. The resulting feature extractor consists of multiple convolution filters, followed by a feature-pooling layer that computes the max of each filter output within adjacent windows, and a point-wise sigmoid non-linearity. A second level of larger and more invariant features is obtained by training the same algorithm on patches of features from the first level. Training a supervised classifier on these features yields 0.64 error on MNIST, and 54 average recognition rate on Caltech 101 with 30 training samples per category. While the resulting architecture is similar to convolutional networks, the layer-wise unsupervised training procedure alleviates the over-parameterization problems that plague purely supervised learning procedures, and yields good performance with very few labeled training samples. In this paper, a feature extraction method for online classification problems is presented by extending Kernel principal component analysis (KPCA). The proposed incremental KPCA (IKPCA) constructs a nonlinear high-dimensional feature space incrementally by not only updating eigen-axes but also adding new eigen-axes. The augmentation of a new eigen-axis is carried out when the accumulation ratio falls below a threshold value. We mathematically derive the incremental update equations of eigen-axes and the accumulation ratio without keeping all training samples. From the experimental results, we conclude that the proposed IKPCA works well as an incremental learning algorithm of a feature space in the sense that a minimum number of axes are augmented to maintain a designated accumulation ratio, and that the eigenvectors with major eigenvalues can converge closely to those of the batch type of KPCA. In addition, the recognition accuracy of IKPCA is similar to or slightly better than that of KPCA Approaches based on local features and descriptors are increasingly used for the task of object recognition due to their robustness with regard to occlusions and geometrical deformations of objects. In this paper we present a local feature based, rotation-invariant Neoperceptron. By extending the weight-sharing properties of convolutional neural networks to orientations, we obtain a neural network that is inherently robust to object rotations, while still being capable to learn optimally discriminant features from training data. The performance of the network is evaluated on a facial expression database and compared to a standard Neoperceptron as well as to the Scale Invariant Feature Transform (SIFT), a-state-of-the-art local descriptor. The results confirm the validity of our approach. Thanks to the powerful feature representations obtained through deep convolutional neural network (CNN), the performance of object detection has recently been substantially boosted. Despite the remarkable success, the problems of object rotation, within-class variability, and between-class similarity remain several major challenges. To address these problems, this paper proposes a novel and effective method to learn a rotation-invariant and Fisher discriminative CNN (RIFD-CNN) model. This is achieved by introducing and learning a rotation-invariant layer and a Fisher discriminative layer, respectively, on the basis of the existing high-capacity CNN architectures. Specifically, the rotation-invariant layer is trained by imposing an explicit regularization constraint on the objective function that enforces invariance on the CNN features before and after rotating. The Fisher discriminative layer is trained by imposing the Fisher discrimination criterion on the CNN features so that they have small within-class scatter but large between-class separation. In the experiments, we comprehensively evaluate the proposed method for object detection task on a public available aerial image dataset and the PASCAL VOC 2007 dataset. State-of-the-art results are achieved compared with the existing baseline methods.