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Dynamic scene classification has been extensively studied in computer vision due to its widespread applications. The key to dynamic scene classification lies in jointly characterizing spatial appearance and temporal dynamics to achieve informative representation, which remains an outstanding task in the literature. In this paper, we propose a unified framework to extract spatial and temporal features for dynamic scene representation. More specifically, we deploy two variants of deep convolutional neural networks to encode spatial appearance and short-term dynamics into short-term deep features (STDF). Based on STDF, we propose using the autoregressive moving average model to extract long-term frequency features (LTFF). By combining STDF and LTFF, we establish the long–short-term feature (LSTF) representations of dynamic scenes. The LSTF characterizes both spatial and temporal patterns of dynamic scenes for comprehensive and information representation that enables more accurate classification. Extensive experiments on three-dynamic scene classification benchmarks have shown that the proposed LSTF achieves high performance and substantially surpasses the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Emotion recognition is challenging due to the emotional gap between emotions and audio–visual features. Motivated by the powerful feature learning ability of deep neural networks, this paper proposes to bridge the emotional gap by using a hybrid deep model, which first produces audio–visual segment features with Convolutional Neural Networks (CNNs) and 3D-CNN, then fuses audio–visual segment features in a Deep Belief Networks (DBNs). The proposed method is trained in two stages. First, CNN and 3D-CNN models pre-trained on corresponding large-scale image and video classification tasks are fine-tuned on emotion recognition tasks to learn audio and visual segment features, respectively. Second, the outputs of CNN and 3D-CNN models are combined into a fusion network built with a DBN model. The fusion network is trained to jointly learn a discriminative audio–visual segment feature representation. After average-pooling segment features learned by DBN to form a fixed-length global video feature, a linear Support Vector Machine is used for video emotion classification. Experimental results on three public audio–visual emotional databases, including the acted RML database, the acted eNTERFACE05 database, and the spontaneous BAUM-1s database, demonstrate the promising performance of the proposed method. To the best of our knowledge, this is an early work fusing audio and visual cues with CNN, 3D-CNN, and DBN for audio–visual emotion recognition. [ABSTRACT FROM AUTHOR]

In this paper, we propose a nonlinear structural hashing approach to learn compact binary codes for scalable video search. Unlike most existing video hashing methods which consider image frames within a video separately for binary code learning, we develop a multi-layer neural network to learn compact and discriminative binary codes by exploiting both the structural information between different frames within a video and the nonlinear relationship between video samples. To be specific, we learn these binary codes under two different constraints at the output of our network: 1) the distance between the learned binary codes for frames within the same scene is minimized and 2) the distance between the learned binary matrices for a video pair with the same label is less than a threshold and that for a video pair with different labels is larger than a threshold. To better measure the structural information of the scenes from videos, we employ a subspace clustering method to cluster frames into different scenes. Moreover, we design multiple hierarchical nonlinear transformations to preserve the nonlinear relationship between videos. Experimental results on three video data sets show that our method outperforms state-of-the-art hashing approaches on the scalable video search task. [ABSTRACT FROM AUTHOR]