Recent developments on sparse dictionary selection have demonstrated promising results for Video Summarization (VS). However, the convex relaxation based solution cannot ensure the sparsity of the dictionary directly. In this paper, a selection matrix is proposed to model the VS problem, according to which the L0norm of this selection matrix is imposed to ensure sparsity directly. As a result, a computational efficient Orthogonal Subspace Projection (OSP) based Iterative Keyframe Selection (IKS) algorithm is proposed for VS. In addition, a Percentage Of Reconstruction (POR) criterion is proposed to provide an intuitive and flexible control of the length of final video summaries even without prior knowledge of a given video. Experimental results on a popular benchmark dataset demonstrate that our proposed algorithm outperforms the state-of-the-art methods.

The ever increasing volume of video content has created profound challenges for developing efficient video summarization (VS) techniques to access the data. Recent developments on sparse dictionary selection have demonstrated promising results for VS, however, the convex relaxation based solution cannot ensure the sparsity of the dictionary directly and it selects keyframes in a local point of view. In this paper, an L2,0constrained sparse dictionary selection model is proposed to reformulate the problem of VS. In addition, a simultaneous orthogonal matching pursuit (SOMP) based method is proposed to obtain an approximate solution for the proposed model without smoothing the penalty function, and thus selects keyframes in a global point of view. In order to allow for intuitive and flexible configuration of VS process, a percentage of residuals (POR) criterion is also developed to produce video summaries in different lengths. Experimental results demonstrate that our proposed method outperforms the state-of-the-art.

Video summarization helps users obtain quick comprehension of video content. Recently, some studies have utilized local features to represent each video frame and formulate video summarization as a coverage problem of local features. However, the importance of individual local features has not been exploited. In this paper, we propose a novel Bag-of-Importance (BoI) model for static video summarization by identifying the frames with important local features as keyframes, which is one of the first studies formulating video summarization at local feature level, instead of at global feature level. That is, by representing each frame with local features, a video is characterized with a bag of local features weighted with individual importance scores and the frames with more important local features are more representative, where the representativeness of each frame is the aggregation of the weighted importance of the local features contained in the frame. In addition, we propose to learn a transformation from a raw local feature to a more powerful sparse nonlinear representation for deriving the importance score of each local feature, rather than directly utilize the hand-crafted visual features like most of the existing approaches. Specifically, we first employ locality-constrained linear coding (LCC) to project each local feature into a sparse transformed space. LCC is able to take advantage of the manifold geometric structure of the high dimensional feature space and form the manifold of the low dimensional transformed space with the coordinates of a set of anchor points. Then we calculate the l2 norm of each anchor point as the importance score of each local feature which is projected to the anchor point. Finally, the distribution of the importance scores of all the local features in a video is obtained as the BoI representation of the video. We further differentiate the importance of local features with a spatial weighting template by taking the perceptual difference among spatial regions of a frame into account. As a result, our proposed video summarization approach is able to exploit both the inter-frame and intra-frame properties of feature representations and identify keyframes capturing both the dominant content and discriminative details within a video. Experimental results on three video datasets across various genres demonstrate that the proposed approach clearly outperforms several state-of-the-art methods.

While most existing video summarization approaches aim to identify important frames of a video from either a global or local perspective, we propose a top-down approach consisting of scene identification and scene summarization. For scene identification, we represent each frame with global features and utilize a scalable clustering method. We then formulate scene summarization as choosing those frames that best cover a set of local descriptors with minimal redundancy. In addition, we develop a visual word-based approach to make our approach more computationally scalable. Experimental results on two benchmark datasets demonstrate that our proposed approach clearly outperforms the state-of-the-art.

Keyframe selection has been crucial for effective and efficient video content analysis. While most of the existing approaches represent individual frames with global features, we, for the first time, propose a keypoint-based framework to address the keyframe selection problem so that local features can be employed in selecting keyframes. In general, the selected keyframes should both be representative of video content and containing minimum redundancy. Therefore, we introduce two criteria, coverage and redundancy, based on keypoint matching in the selection process. Comprehensive experiments demonstrate that our approach outperforms the state of the art.

Image annotation has been widely investigated to discover the semantics of an image. However, most of the existing algorithms focus on noun tags (e.g. concepts and objects). Since an image is a snapshot of the real world event, annotating images with verbs will enable richer understanding of an image. In this paper, we propose a data-driven approach to verb oriented image annotation. At first, we obtain verb candidates by generating search queries for a given image with initial noun tags and establishing a sentence corpus from those queries. We utilize visualness to filter tags which are not visually presentable (e.g. pain) and differentiate tags into two categories (i.e. scene based and object based) to impose linguistic rules in verb extraction. Then we further re-rank the candidate verbs with the tag context discovered from the images which are both semantically and visually similar to the given image in the MIRFlickr dataset. Our experimental results from user study demonstrate that our proposed approach is promising.

In this demo, we develop the StoryImaging system to illustrate a textual story with both images harvested from the Web and synthesized speech. At the backend, a story is firstly processed to identify key terms such as named entities and to obtain the story summary. With the aid of commercial search engines, images are then collected from the Web for those key terms and re-ranked by taking the summary as context. At last, images are clustered to provide an overview of the story. At the web-based frontend, the user interface has been tailored to both improve information comprehension and provide engaging and explorative experiences for users by closely bridging textual and visual modalities.

Exploring semantic similarity between concepts in visual domain has a wide range of applications such as natural language processing and multimedia retrieval, which in general requires both a large pool of sample images for each concept and a model to capture its visual characteristics. Instead of relying on high quality and large quantity sample data which is very difficult to obtain, in this paper, a novel method is proposed to achieve improvement in measuring concept similarity by incorporating concept modeling technique into data pruning process. At first, a number of sampling concept models are obtained by sampling a subset from the sample dataset of each concept. Then noisy samples are discarded in terms of their probabilities to the sampling concept models. Experimental results on 31,275 Web images of 38 concepts defined in LSCOM indicate that the concept similarity obtained through our proposed approach is more coherent to human cognition. A concept hierarchy tree built from the 38 concepts with their similarity further demonstrates the effectiveness of our proposed method.

Concept similarity has been intensively researched in the natural language processing domain due to its important role in many applications such as language modeling and information retrieval. There are few studies on measuring concept similarity in visual domain, though concept based multimedia information retrieval has attracted a lot of attentions. In this paper, we present a scalable framework for such a purpose, which is different from traditional approaches to exploring correlation among concepts in image/video annotation domain. For each concept, a model based on feature distribution is built using sample images collected from the Internet. And similarity between concepts is measured with the similarity between their models. Hereby, a Gaussian Mixture Model (GMM) is employed to model each concept and two similarity measurements are investigated. Experimental results on 13,974 images of 16 concepts collected through image search engines have demonstrated that the similarity between concepts is very close to human perception. In addition, the entropy of GMM cluster distributions can be a good indication of selecting concepts for image/video annotation.