Exploiting the temporal dependency among video frames or subshots is very important for the task of video summarization. Practically, RNN is good at temporal dependency modeling, and has achieved overwhelming performance in many video-based tasks, such as video captioning and classification. However, RNN is not capable enough to handle the video summarization task, since traditional RNNs, including LSTM, can only deal with short videos, while the videos in the summarization task are usually in longer duration. To address this problem, we propose a hierarchical recurrent neural network for video summarization, called H-RNN in this paper. Specifically, it has two layers, where the first layer is utilized to encode short video subshots cut from the original video, and the final hidden state of each subshot is input to the second layer for calculating its confidence to be a key subshot. Compared to traditional RNNs, H-RNN is more suitable to video summarization, since it can exploit long temporal dependency among frames, meanwhile, the computation operations are significantly lessened. The results on two popular datasets, including the Combined dataset and VTW dataset, have demonstrated that the proposed H-RNN outperforms the state-of-the-arts.
Unsupervised approaches select key subshots according to manually designed criteria @cite_32 @cite_36 @cite_17 @cite_28 , such as representative to the video content and diverse with each other, etc. Clustering is one of the most popular unsupervised summarization approaches @cite_28 @cite_37 @cite_19 . Practically, with hand-crafted features, similar frames are grouped into the same cluster, and the cluster centers are taken as the most representative elements and selected into the summary. Earlier works apply clustering algorithms to video summarization directly @cite_4 @cite_34 . Later, more works integrate the domain knowledge of video data into clustering algorithms @cite_19 @cite_28 . As in @cite_19 , the frames are initially clustered in sequential order, as consecutive frames are similar and more probably to be allocated to the same cluster. Other works construct more comprehensive models based on the idea of clustering @cite_36 @cite_11 . As in @cite_36 , the video is transformed into an undirected graph, and the summary is generated by partitioning this graph into clusters. More recently, a co-clustering approach is proposed to simultaneously summarize several videos with the same topic by their co-occurrence, i.e., similar subshots shared by these videos are selected into the summary @cite_11 .
@cite_37 @cite_11 @cite_4 @cite_36 @cite_28 @cite_32 @cite_19 @cite_34 @cite_17
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We propose a unified approach for video summarization based on the analysis of video structures and video highlights. Two major components in our approach are scene modeling and highlight detection. Scene modeling is achieved by normalized cut algorithm and temporal graph analysis, while highlight detection is accomplished by motion attention modeling. In our proposed approach, a video is represented as a complete undirected graph and the normalized cut algorithm is carried out to globally and optimally partition the graph into video clusters. The resulting clusters form a directed temporal graph and a shortest path algorithm is proposed to efficiently detect video scenes. The attention values are then computed and attached to the scenes, clusters, shots, and subshots in a temporal graph. As a result, the temporal graph can inherently describe the evolution and perceptual importance of a video. In our application, video summaries that emphasize both content balance and perceptual quality can be generated directly from a temporal graph that embeds both the structure and attention information. This paper is the first to address the problem of unsupervised action localization in videos. Given unlabeled data without bounding box annotations, we propose a novel approach that: 1) Discovers action class labels and 2) Spatio-temporally localizes actions in videos. It begins by computing local video features to apply spectral clustering on a set of unlabeled training videos. For each cluster of videos, an undirected graph is constructed to extract a dominant set, which are known for high internal homogeneity and in-homogeneity between vertices outside it. Next, a discriminative clustering approach is applied, by training a classifier for each cluster, to iteratively select videos from the non-dominant set and obtain complete video action classes. Once classes are discovered, training videos within each cluster are selected to perform automatic spatio-temporal annotations, by first over-segmenting videos in each discovered class into supervoxels and constructing a directed graph to apply a variant of knapsack problem with temporal constraints. Knapsack optimization jointly collects a subset of supervoxels, by enforcing the annotated action to be spatio-temporally connected and its volume to be the size of an actor. These annotations are used to train SVM action classifiers. During testing, actions are localized using a similar Knapsack approach, where supervoxels are grouped together and SVM, learned using videos from discovered action classes, is used to recognize these actions. We evaluate our approach on UCF-Sports, Sub-JHMDB, JHMDB, THUMOS13 and UCF101 datasets. Our experiments suggest that despite using no action class labels and no bounding box annotations, we are able to get competitive results to the state-of-the-art supervised methods. Recent progresses in multimedia event detection have enabled us to find videos about a predefined event from a large-scale video collection. Research towards more intrinsic unsupervised video understanding is an interesting but understudied field. Specifically, given a collection of videos sharing a common event of interest, the goal is to discover the salient fragments, i.e., the curt video fragments that can concisely portray the underlying event of interest, from each video. To explore this novel direction, this paper proposes an unsupervised event saliency revealing framework. It first extracts features from multiple modalities to represent each shot in the given video collection. Then, these shots are clustered to build the cluster-level event saliency revealing framework, which explores useful information cues (i.e., the intra-cluster prior, inter-cluster discriminability, and inter-cluster smoothness) by a concise optimization model. Compared with the existing methods, our approach could highlight the intrinsic stimulus of the unseen event within a video in an unsupervised fashion. Thus, it could potentially benefit to a wide range of multimedia tasks like video browsing, understanding, and search. To quantitatively verify the proposed method, we systematically compare the method to a number of baseline methods on the TRECVID benchmarks. Experimental results have demonstrated its effectiveness and efficiency. We propose a unified approach for summarization based on the analysis of video structures and video highlights. Our approach emphasizes both the content balance and perceptual quality of a summary. Normalized cut algorithm is employed to globally and optimally partition a video into clusters. A motion attention model based on human perception is employed to compute the perceptual quality of shots and clusters. The clusters, together with the computed attention values, form a temporal graph similar to Markov chain that inherently describes the evolution and perceptual importance of video clusters. In our application, the flow of a temporal graph is utilized to group similar clusters into scenes, while the attention values are used as guidelines to select appropriate subshots in scenes for summarization. In this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. This algorithm is based on the k-medoid clustering algorithms to find the best representative frame for each video shot. This algorithm, which is applicable to all types of descriptors, consists of extracting key frames by similarity clustering according to the given index. In our proposal, the distance between frames is calculated using a fast full search block matching algorithm based on the frequency domain. The proposed approach is computationally tractable and robust with respect to sudden changes in mean intensity within a shot. Additionally, this approach produces different key frames even in the presence of large motion. The experiments results show that our algorithm extracts multiple representatives frames in each video shot without visual redundancy, and thus it is an effective tool for video indexing and retrieval. The goal of this paper is unsupervised face clustering in edited video material – where face tracks arising from different people are assigned to separate clusters, with one cluster for each person. In particular we explore the extent to which faces can be clustered automatically without making an error. This is a very challenging problem given the variation in pose, lighting and expressions that can occur, and the similarities between different people. The novelty we bring is three fold: first, we show that a form of weak supervision is available from the editing structure of the material – the shots, threads and scenes that are standard in edited video; second, we show that by first clustering within scenes the number of face tracks can be significantly reduced with almost no errors; third, we propose an extension of the clustering method to entire episodes using exemplar SVMs based on the negative training data automatically harvested from the editing structure. The method is demonstrated on multiple episodes from two very different TV series, Scrubs and Buffy. For both series it is shown that we move towards our goal, and also outperform a number of baselines from previous works. We present video co-summarization, a novel perspective to video summarization that exploits visual co-occurrence across multiple videos. Motivated by the observation that important visual concepts tend to appear repeatedly across videos of the same topic, we propose to summarize a video by finding shots that co-occur most frequently across videos collected using a topic keyword. The main technical challenge is dealing with the sparsity of co-occurring patterns, out of hundreds to possibly thousands of irrelevant shots in videos being considered. To deal with this challenge, we developed a Maximal Biclique Finding (MBF) algorithm that is optimized to find sparsely co-occurring patterns, discarding less co-occurring patterns even if they are dominant in one video. Our algorithm is parallelizable with closed-form updates, thus can easily scale up to handle a large number of videos simultaneously. We demonstrate the effectiveness of our approach on motion capture and self-compiled YouTube datasets. Our results suggest that summaries generated by visual co-occurrence tend to match more closely with human generated summaries, when compared to several popular unsupervised techniques. Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay graph clustering via an iterative edge pruning strategy. A structural constraint in form of a lower limit on the deviation ratio of the graph vertices further improves the video summary. We also employ an information-theoretic pre-sampling where significant valleys in the mutual information profile of the successive frames in a video are used to capture more informative frames. Various video key frame visualization techniques for efficient video browsing and navigation purposes are incorporated. A comprehensive evaluation on 100 videos from the Open Video and YouTube databases using both objective and subjective measures demonstrate the superiority of our key frame extraction method. We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the task as a structured prediction problem, our main idea is to use Long Short-Term Memory (LSTM) to model the variable-range temporal dependency among video frames, so as to derive both representative and compact video summaries. The proposed model successfully accounts for the sequential structure crucial to generating meaningful video summaries, leading to state-of-the-art results on two benchmark datasets. In addition to advances in modeling techniques, we introduce a strategy to address the need for a large amount of annotated data for training complex learning approaches to summarization. There, our main idea is to exploit auxiliary annotated video summarization datasets, in spite of their heterogeneity in visual styles and contents. Specifically, we show that domain adaptation techniques can improve learning by reducing the discrepancies in the original datasets’ statistical properties.