The covariance descriptor which is a symmetric positive definite (SPD) matrix, has recently attracted considerable attentions in computer vision. However, it is not trivial issue to handle its non-linearity in semi-supervised learning. To this end, in this paper, a semi-supervised sparse subspace clustering on SPD manifolds is proposed, via considering the intrinsic geometric structure within the manifold-valued data. Experimental results on two databases show that our method can provide better clustering solutions than the state-of-the-art approaches thanks to incorporating Riemannian geometry structure.
This paper considers the problem of clustering uncertain objects whose locations are uncertain and described by probability density functions (pdf). Though K-means has been extended to UK-means for handling uncertain data, most existing works only focus on improving the efficiency of UK-means. However, the clustering quality of UK-means is rarely considered in existing works. The weights of objects are assumed same in existing works. However, the weights of objects which are far from their cluster representatives should not be the same as the weights of objects which are close to their cluster representatives. Thus, we propose an AdaUK-means to group the uncertain objects by considering the weights of objects in this article. In AdaUK-means, the weights of objects will be adjusted based on the correlation between objects by using Adaboost. If the object pairs are must-link but grouped into different clusters, the weights of the objects will be increased. In our ensemble model, AdaUK-means is run several times, then the objects are assigned by a voting process. Finally, we demonstrate that AdaUK-means performs better than UK-means on both synthetic and real data sets by extensive experiments.
A novel semantic segmentation method is proposed, which consists of the following three parts: (I) First, a simple yet effective data augmentation method is introduced without any extra GPU memory cost during training. (II) Second, a deeper residual network is constructed through three effective techniques: dilated convolution, LSTM network and multi-scale prediction. (III) Third, an online hard pixels mining is adopted to improve the segmentation performance. We combine these three parts to train an end-to-end network and achieve a new state-of-the-art segmentation accuracy of 79.3% on PASCAL VOC 2012 test set at the time of submission.
Structure information is a hot spot currently in the domain of computer vision. As many people had applied structure information to their method, few people employed degradation information to their algorithm. However the degradation itself contains some important information. In this paper, we introduce a Structured Degradation Model with degradation assessment of the target to solve the tracking problem. To track the target in non-uniform degraded video, autocorrelation is used to generate the direction map and Tenengrad is used to extract the degradation degree of each target part. In our Structured Degradation Model, an undirected graph of the target is generated to track the target. The nodes of the graph are the target parts and the edges are the interactions between the parts. Experimental result shows that our method performs well especially for object tracking in degraded video.
License plate detection is a crucial part in license plate recognition systems and is often considered as a solved problem. However, there are still plenty of complex scenes where the current methods are invalidated. In order to increase the performance in these scenes, we propose a novel character-based method to detect multiple license plates in complex images. Firstly, a preprocessing step is performed. Then we use a modified maximally stable extremal region (MSER) based detector called MSER-+ to detect the possible character regions. Some of the regions are removed according to their geographical information. Hierarchical morphology helps to connect candidate MSERs of various sizes. The regions satisfying some geographical limits will be fed into a convolutional neural network (CNN) model for further verification. Extensive experimental results validate that our method works well in a large variety of complex scenes.
