Co-Regularized Deep Representations for Video Summarization

Compact keyframe-based video summaries are a popular way of generating viewership on video sharing platforms. Yet, creating relevant and compelling summaries for arbitrarily long videos with a small number of keyframes is a challenging task. We propose a comprehensive keyframe-based summarization framework combining deep convolutional neural networks and restricted Boltzmann machines. An original co-regularization scheme is used to discover meaningful subject-scene associations. The resulting multimodal representations are then used to select highly-relevant keyframes. A comprehensive user study is conducted comparing our proposed method to a variety of schemes, including the summarization currently in use by one of the most popular video sharing websites. The results show that our method consistently outperforms the baseline schemes for any given amount of keyframes both in terms of attractiveness and informativeness. The lead is even more significant for smaller summaries.Comment: Video summarization, deep convolutional neural networks, co-regularized restricted Boltzmann machine

Comparison of Balancing Techniques for Multimedia IR over Imbalanced Datasets

A promising method to improve the performance of information retrieval systems is to approach retrieval tasks as a supervised classification problem. Previous user interactions, e.g. gathered from a thorough log file analysis, can be used to train classifiers which aim to inference relevance of retrieved documents based on user interactions. A problem in this approach is, however, the large imbalance ratio between relevant and non-relevant documents in the collection. In standard test collection as used in academic evaluation frameworks such as TREC, non-relevant documents outnumber relevant documents by far. In this work, we address this imbalance problem in the multimedia domain. We focus on the logs of two multimedia user studies which are highly imbalanced. We compare a naiinodotve solution of randomly deleting documents belonging to the majority class with various balancing algorithms coming from different fields: data classification and text classification. Our experiments indicate that all algorithms improve the classification performance of just deleting at random from the dominant class

A walk through the web’s video clips

Approximately 10^5 video clips are posted every day on the Web. The popularity of Web-based video databases poses a number of challenges to machine vision scientists: how do we organize, index and search such large wealth of data? Content-based video search and classification have been proposed in the literature and applied successfully to analyzing movies, TV broadcasts and lab-made videos. We explore the performance of some of these algorithms on a large data-set of approximately 3000 videos. We collected our data-set directly from the Web minimizing bias for content or quality, way so as to have a faithful representation of the statistics of this medium. We find that the algorithms that we have come to trust do not work well on video clips, because their quality is lower and their subject is more varied. We will make the data publicly available to encourage further research

Hierarchical video summarisation in reference frame subspace

In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using k-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently

Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

Design of Video Retrieval System Using MPEG-7 Descriptors

AbstractThe paper proposes a content-based video retrieval system designed using MPEG-7 (multimedia content description interface), which provides a standard description for a video. The system consists of three parts: shot boundary detection, feature extraction and similarity measurement. In shot boundary detection, cut and dissolve can be detected using the histogram difference and skipping image difference, respectively. In feature extraction part, two MPEG-7 visual descriptors, Color Structure Descriptor (CSD) and Edge Histogram Descriptor (EHD), are used to represent the color feature and edge feature of the key frames. Lastly, the similarity between key frames is calculated using dynamic-weighted feature similarity calculation. The proposed system is tested on three kinds of videos. Promising results are obtained in terms of both effectiveness and efficiency

On the surplus value of semantic video analysis beyond the key frame

Typical semantic video analysis methods aim for classification of camera shots based on extracted features from a single key frame only. In this paper, we sketch a video analysis scenario and evaluate the benefit of analysis beyond the key frame for semantic concept detection performance. We developed detectors for a lexicon of 26 concepts, and evaluated their performance on 120 hours of video data. Results show that, on average, detection performance can increase with almost 40 % when the analysis method takes more visual content into account. 1