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Video summarization (VS) is generally formulated as a subset selection problem where a set of representative keyframes or key segments is selected from an entire video frame set. Though many sparse subset selection based VS algorithms have been proposed in the past decade, most of them adopt linear sparse formulation in the explicit feature vector space of video frames, and don’t consider the local or global relationships among frames. In this paper, we first extend the conventional sparse subset selection for VS into kernel block sparse subset selection (KBS3) to utilize the advantage of kernel sparse coding and introduce a local inter-frame relationship through packing of frame blocks. Going a step further, we propose a similarity based block sparse subset selection (SB2S3) model by applying a specially designed transformation matrix on the KBS3 model in order to introduce a kind of global inter-frame relationship through the similarity. Finally, a greedy pursuit based algorithm is devised for the proposed NP-hard model optimization. The proposed SB2S3 has the following advantages: 1) through the similarity between each frame and any other frame, the global relationship among all frames can be considered; 2) through block sparse coding, the local relationship of adjacent frames is further considered; and 3) it has a wider application, since features can derive similarity, but not vice versa. It is believed that the effect of modeling such global and local relationships among frames in this paper, is similar to that of modeling the long-range and short-range dependencies among frames in deep learning based methods. Experimental results on three benchmark datasets have demonstrated that the proposed approach is superior to not only other sparse subset selection based VS methods but also most unsupervised deep-learning based VS methods. [ABSTRACT FROM AUTHOR]

Rotation-invariant features are of great importance for object detection and image classification in very-high-resolution (VHR) optical remote sensing images. Though multibranch convolutional neural network (mCNN) has been demonstrated to be very effective for rotation-invariant feature learning, how to effectively train such a network is still an open problem. In this article, a nested Siamese structure (NSS) is proposed for training the mCNN to learn effective rotation-invariant features, which consists of an inner Siamese structure to enhance intraclass cohesion and an outer Siamese structure to enlarge interclass margin. Moreover, a double center loss (DCL) function, in which training samples from the same class are mapped closer to each other while those from different classes are mapped far away to each other, is proposed to train the proposed NSS even with a small amount of training samples. Experimental results over three benchmark data sets demonstrate that the proposed NSS trained by DCL is very effective to encounter rotation varieties when learning features for image classification and outperforms several state-of-the-art rotation-invariant feature learning algorithms even when a small amount of training samples are available. [ABSTRACT FROM AUTHOR]

In recent years, sparse representation has been successfully utilized for video summarization (VS). However, most of the sparse representation based VS methods characterize each video frame with global features. As a result, some important local details could be neglected by global features, which may compromise the performance of summarization. In this paper, we propose to partition each video frame into a number of patches and characterize each patch with global features. Instead of concatenating the features of each patch and utilizing conventional sparse representation, we formulate the VS problem with such video frame representation as block sparse representation by considering each video frame as a block containing a number of patches. By taking the reconstruction constraint into account, we devise a simultaneous version of block-based OMP (Orthogonal Matching Pursuit) algorithm, namely SBOMP, to solve the proposed model. The proposed model is further extended to a neighborhood based model which considers temporally adjacent frames as a super block. This is one of the first sparse representation based VS methods taking both spatial and temporal contexts into account with blocks. Experimental results on two widely used VS datasets have demonstrated that our proposed methods present clear superiority over existing sparse representation based VS methods and are highly comparable to some deep learning ones requiring supervision information for extra model training. [ABSTRACT FROM AUTHOR]

Parkinson's disease significantly impacts the life quality of millions of people around the world. While freezing of gait (FoG) is one of the most common symptoms of the disease, it is time consuming and subjective to assess FoG for well-trained experts. Therefore, it is highly desirable to devise computer-aided FoG detection methods for the purpose of objective and time-efficient assessment. In this paper, in line with the gold standard of FoG clinical assessment, which requires video or direct observation, we propose one of the first vision-based methods for automatic FoG detection. To better characterize FoG patterns, instead of learning an overall representation of a video, we propose a novel architecture of graph convolution neural network and represent each video as a directed graph where FoG related candidate regions are the vertices. A weakly-supervised learning strategy and a weighted adjacency matrix estimation layer are proposed to eliminate the resource expensive data annotation required for fully supervised learning. As a result, the interference of visual information irrelevant to FoG, such as gait motion of supporting staff involved in clinical assessments, has been reduced to improve FoG detection performance by identifying the vertices contributing to FoG events. To further improve the performance, the global context of a clinical video is also considered and several fusion strategies with graph predictions are investigated. Experimental results on more than 100 videos collected from 45 patients during a clinical assessment demonstrated promising performance of our proposed method with an AUC of 0.887. [ABSTRACT FROM AUTHOR]

Feature learning technologies using convolutional neural networks (CNNs) have shown superior performance over traditional hand-crafted feature extraction algorithms. However, a large number of labeled samples are generally required for CNN to learn effective features under classification task, which are hard to be obtained for hyperspectral remote sensing images. Therefore, in this paper, an unsupervised spatial–spectral feature learning strategy is proposed for hyperspectral images using 3-Dimensional (3D) convolutional autoencoder (3D-CAE). The proposed 3D-CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial–spectral structure information for feature extraction. A companion 3D convolutional decoder network is also designed to reconstruct the input patterns to the proposed 3D-CAE, by which all the parameters involved in the network can be trained without labeled training samples. As a result, effective features are learned in an unsupervised mode that label information of pixels is not required. Experimental results on several benchmark hyperspectral data sets have demonstrated that our proposed 3D-CAE is very effective in extracting spatial–spectral features and outperforms not only traditional unsupervised feature extraction algorithms but also many supervised feature extraction algorithms in classification application. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new framework for hyperspectral image classification, namely pseudolabel guided kernel learning (PLKL). The proposed framework is capable of fully utilizing unlabeled samples, making it very effective to handle the task with extremely limited training samples. Specifically, with multiple initial kernels and labeled samples, we first employ support vector machine (SVM) classifiers to predict pseudolabels independently for each unlabeled sample, and consistency voting is applied to the resulting pseudolabels to select and add a few unlabeled samples to the training set. Then, we refine the kernels to improve their discriminability with the augmented training set and a typical kernel learning method. Such phases are repeated until stable. Furthermore, we enhance the PLKL in terms of both the computation and memory efficiencies by using a bagging-like strategy, improving its practicality for large scale datasets. In addition, the proposed framework is quite flexible and general. That is, other advanced kernel-based methods can be incorporated to continuously improve the performance. Experimental results show that the proposed frameworks achieve much higher classification accuracy, compared with state-of-the-art methods. Especially, the classification accuracy improves more than 5% with very few training samples. [ABSTRACT FROM AUTHOR]