Collaborative Summarization of Topic-Related Videos

Large collections of videos are grouped into clusters by a topic keyword, such as Eiffel Tower or Surfing, with many important visual concepts repeating across them. Such a topically close set of videos have mutual influence on each other, which could be used to summarize one of them by exploiting information from others in the set. We build on this intuition to develop a novel approach to extract a summary that simultaneously captures both important particularities arising in the given video, as well as, generalities identified from the set of videos. The topic-related videos provide visual context to identify the important parts of the video being summarized. We achieve this by developing a collaborative sparse optimization method which can be efficiently solved by a half-quadratic minimization algorithm. Our work builds upon the idea of collaborative techniques from information retrieval and natural language processing, which typically use the attributes of other similar objects to predict the attribute of a given object. Experiments on two challenging and diverse datasets well demonstrate the efficacy of our approach over state-of-the-art methods.Comment: CVPR 201

Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

In the past decade, deep learning techniques have powered many aspects of our daily life, and drawn ever-increasing research interests. However, conventional deep learning approaches, such as deep belief network (DBN), restricted Boltzmann machine (RBM), and convolutional neural network (CNN), suffer from time-consuming training process due to fine-tuning of a large number of parameters and the complicated hierarchical structure. Furthermore, the above complication makes it difficult to theoretically analyze and prove the universal approximation of those conventional deep learning approaches. In order to tackle the issues, multilayer extreme learning machines (ML-ELM) were proposed, which accelerate the development of deep learning. Compared with conventional deep learning, ML-ELMs are non-iterative and fast due to the random feature mapping mechanism. In this paper, we perform a thorough review on the development of ML-ELMs, including stacked ELM autoencoder (ELM-AE), residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications. In addition, we also discuss the connection between random neural networks and conventional deep learning

SULoRA: Subspace Unmixing with Low-Rank Attribute Embedding for Hyperspectral Data Analysis

To support high-level analysis of spaceborne imaging spectroscopy (hyperspectral) imagery, spectral unmixing has been gaining significance in recent years. However, from the inevitable spectral variability, caused by illumination and topography change, atmospheric effects and so on, makes it difficult to accurately estimate abundance maps in spectral unmixing. Classical unmixing methods, e.g. linear mixing model (LMM), extended linear mixing model (ELMM), fail to robustly handle this issue, particularly facing complex spectral variability. To this end, we propose a subspace-based unmixing model using low-rank learning strategy, called subspace unmixing with low-rank attribute embedding (SULoRA), robustly against spectral variability in inverse problems of hyperspectral unmixing. Unlike those previous approaches that unmix the spectral signatures directly in original space, SULoRA is a general subspace unmixing framework that jointly estimates subspace projections and abundance maps in order to find a ‘raw’ subspace which is more suitable for carrying out the unmixing procedure. More importantly, we model such ‘raw’ subspace with low-rank attribute embedding. By projecting the original data into a low-rank subspace, SULoRA can effectively address various spectral variabilities in spectral unmixing. Furthermore, we adopt an alternating direction method of multipliers (ADMM) based to solve the resulting optimization problem. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods