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.

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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

Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.

A Survey on Visual Content-Based Video Indexing and Retrieval

Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras. This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

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Thermal cameras and applications: a survey

This paper presents a brief overview of past and current hardware acceleration (HwA) approaches that have been proposed for the most computationally intensive compression tools of the MPEG-4 standard. These approaches are classified based on their historical evolution and architectural approach. An analysis of both evolutionary and functional classifications is carried out in order to speculate on the possible trends of the HwA architectures to be employed in mobile video platforms.

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Hardware acceleration architectures for MPEG-Based mobile video platforms: a brief overview

In today's fast paced world, the time available to watch long sports programmes is decreasing, while the number of sports channels is rapidly increasing. Many viewers desire the facility to watch just the highlights of sports events. This paper presents a simple, but effective, method for generating sports video highlights summaries. Our method detects semantically important events in sports programmes by using the Scale Factors in the MPEG audio bitstream to generate an audio amplitude profile of the program. The Scale Factors for the sub- bands corresponding to the voice bandwidth give a strong indication of the level of commentator and/or spectator excitement. When periods of sustained high audio amplitude have been detected and ranked, the corresponding video shots may be concatenated to produce a summary of the program highlights. Our method uses only the Scale Factor information that is directly accessible from the MPEG bitstream, without any decoding, leading to highly efficient computation. It is also rather more generic than many existing techniques, being particularly suitable for the more popular sports televised in Ireland such as soccer, Gaelic football, hurling, rugby, horse racing and motor racing.

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Audio processing for automatic TV sports program highlights detection

This paper gives an overview of low-power techniques proposed in the literature for mobile multimedia and Internet appli- cations. Exploitable aspects are discussed in the behavior of dier- ent video compression tools. These power-ecient solutions are then classified by synthesis domain and level of abstraction. As this paper is meant to be a starting point for further research in the area, a low- power hardware& software co-design methodology is outlined in the end as a possible scenario for video-codec-on-a-chip implementations on future mobile multimedia platforms. Keywords — mobile multimedia, video compression, low power, hardware acceleration

Low Power Techniques for Video Compression

Abstrad - In this paper we present an algorithm for automatic classification of sound into speech, instrumental sound/music and silence. The method is based on thresholding of features derived from the modulation envelope of the frequency limited audio signal. Four characteristics are examined for discrimination: the occurrence and duration of energy peaks, rhythmic content and the level of harmonic content The proposed algorithm allows classification directly on MAEG-1 audio bitstreams. The performance of the classifier was evaluated on TRECVID test data The test results are above-average among all TREC participants. The approaches adopted by other research groups participating in TREC are also discussed.

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An experiment in audio classification from compressed data

In this paper, an efficient tool to extract video objects from video sequences is presented. With this tool, it is possible to segment video content in a user-friendly manner to provide easy manipulation of video content. The tool is comprised of two stages. Firstly, the initial object extraction is performed using the Recursive Shortest Spanning Tree (RSST) algorithm and the Binary Partition Tree (BPT) technique. Secondly, automatic object tracking is performed using a single frame forward region tracking method. In the first stage, an initial partition is created using the RSST algorithm which allows the user to specify the initial number of regions. This process is followed by progressive binary merging of these regions to create the BPT. The purpose of creating the BPT is to allow the user to browse the content of the scene in a hierarchical manner. This merging step creates the binary tree with nearly double the user-specified number of homogenous regions. User interaction then allows grouping particular regions into objects. In the second stage, each subsequent frame is segmented using the RSST and corresponding regions are identified using a forward region tracking method.

Noel Murphy

Papers

Hardware acceleration architectures for MPEG-Based mobile video platforms: a brief overview

Audio processing for automatic TV sports program highlights detection

Low Power Techniques for Video Compression

An experiment in audio classification from compressed data

Hierarchical semiautomatic video object segmentation for multimedia applications