In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.

/pdf/trust-in-recommender-systems-1f8engmkbd.pdf

Trust in recommender systems

The demand for various multimedia applications is rapidly increasing due to the recent advance in the computing and network infrastructure, together with the widespread use of digital video technology. Among the key elements for the success of these applications is how to effectively and efficiently manage and store a huge amount of audio visual information, while at the same time providing user-friendly access to the stored data. This has fueled a quickly evolving research area known as video abstraction. As the name implies, video abstraction is a mechanism for generating a short summary of a video, which can either be a sequence of stationary images (keyframes) or moving images (video skims). In terms of browsing and navigation, a good video abstract will enable the user to gain maximum information about the target video sequence in a specified time constraint or sufficient information in the minimum time. Over past years, various ideas and techniques have been proposed towards the effective abstraction of video contents. The purpose of this article is to provide a systematic classification of these works. We identify and detail, for each approach, the underlying components and how they are addressed in specific works.

Video abstraction: A systematic review and classification

A media content description system receives media content descriptions from one or more metadata providers. The media content description system associates each media content description with the metadata provider that provided the description. The media content description system may generate composite descriptions based on the received media content descriptions. The media content description system provides data associated with the media content descriptions to one or more program data providers.

Media content descriptions

Wireless transmission of a single bit can require over 1000 times more energy than a single computation. It can therefore be beneficial to perform additional computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, there is a net energy savings and an increase in battery life for portable computers. This article presents a study of the energy savings possible by losslessly compressing data prior to transmission. A variety of algorithms were measured on a StrongARM SA-110 processor. This work demonstrates that, with several typical compression algorithms, there is a actually a net energy increase when compression is applied before transmission. Reasons for this increase are explained and suggestions are made to avoid it. One such energy-aware suggestion is asymmetric compression, the use of one compression algorithm on the transmit side and a different algorithm for the receive path. By choosing the lowest-energy compressor and decompressor on the test platform, overall energy to send and receive data can be reduced by 11p compared with a well-chosen symmetric pair, or up to 57p over the default symmetric zlib scheme.

Energy-aware lossless data compression

The development of standards for video encoding coupled with the increased power of computing mean that content-based manipulation of digital video information is now feasible. Shots are a basic structural building block of digital video and the boundaries between shots need to be determined automatically to allow for content-based manipulation. A shot can be thought of as continuous images from one camera at a time. In this paper we examine a variety of automatic techniques for shot boundary detection that we have implemented and evaluated on a baseline of 720,000 frames (8 hours) of broadcast television. This extends our previous work on evaluating a single technique based on comparing colour histograms. A description of each of our three methods currently working is given along with how they
are evaluated. It is found that although the different methods have about the same order of magnitude in terms of effectiveness, different shot boundaries are detected by the different methods. We then look at combining the three shot boundary detection methods to produce one output result and the benefits in accuracy and performance that this brought to our system. Each of the methods were changed from using
a static threshold value for three unconnected methods to one using three dynamic threshold values for one connected method. In a final summing up we look at the future directions for this work.

/pdf/evaluating-and-combining-digital-video-shot-boundary-28k1t4xbx8.pdf

Evaluating and combining digital video shot boundary detection algorithms

Automatic TV advertisement detection from MPEG bitstream

The challenge facing the indexing of digital video information in order to support browsing and retrieval by users, is to design systems that can accurately and automatically process large amounts of heterogeneous video. The segmentation of video material into shots and scenes is the basic operation in the analysis of video content. This paper presents a detailed evaluation of a histogram-based shot cut detector based on eight hours of TV broadcast video. Our observations are that the selection of similarity thresholds for determining shot boundaries in such broadcast video is difficult and necessitates the development of systems that employ adaptive thresholding in order to address the huge variation of characteristics prevalent in TV broadcast video.

/pdf/evaluation-of-automatic-shot-boundary-detection-on-a-large-4rc9yoyqdj.pdf

Evaluation of automatic shot boundary detection on a large video test suite

In digital video indexing research area an important technique is called shot boundary detection which automatically segments long video material into camera shots using content-based analysis of video We have been working on developing various shot boundary detection and representative frame selection techniques to automatically index encoded video stream and provide the end users with video browsing/navigation feature In this paper we describe a demonstrator digital video system that allows the user to record a TV broadcast programme to MPEG-1 file format and to easily browse and playback the file content online The system incorporates the shot boundary detection and representative frame selection techniques we have developed and has become a full-featured digital video system that not only demonstrates any further techniques we will develop, but also obtains users' video browsing behaviour At the moment the system has a real-user base of about a hundred people and we are closely monitoring how they use the video browsing/navigation feature wliich the system provides

/pdf/the-fischiar-digital-video-recording-analysis-and-browsing-2sh1gwyrqg.pdf

The físchiár digital video recording, analysis and browsing system

This paper presents an approach to segmenting individual news stories in broadcast news programmes. The approach first performs shot boundary detection and keyframe extraction on the programme. Shots are then clustered into groups based on their colour and temporal similarity. The clustering process is controlled using the groups' statistics. After clustering, a set of criteria are applied and groups are successively eliminated in order to converge upon a set of anchorperson groups. The temporal locations of the shots in these anchorperson groups are then used to segment the programme in terms of individual news items. This work is carried out within the context of a complete video indexing, browsing and retrieval system.

/pdf/news-story-segmentation-in-the-fischlar-video-indexing-2z6je8dtk1.pdf

Seán Marlow

Papers

Evaluating and combining digital video shot boundary detection algorithms

Automatic TV advertisement detection from MPEG bitstream

Evaluation of automatic shot boundary detection on a large video test suite

The físchiár digital video recording, analysis and browsing system

News story segmentation in the Fischlar video indexing system