Matthew Roach

Bio: Matthew Roach is an academic researcher from Swansea University. The author has contributed to research in topics: Motion estimation & Motion compensation. The author has an hindex of 9, co-authored 14 publications receiving 366 citations. Previous affiliations of Matthew Roach include University of Wales.

Video genre classification using dynamics

Abstract: The problem addressed here is the classification of videos at the highest level into pre-defined genre. The approach adopted is based on the dynamic content of short sequences (/spl sim/30 secs). This paper presents two methods of extracting motion from a video sequence: foreground object motion and background camera motion. These dynamics are extracted, processed and applied to classify 3 broad classes: sports, cartoons and news. Experimental results for this 3 class problem give error rates of 17%, 8% and 6% for camera motion, object motion and both combined respectively, on /spl sim/30 second sequences.

Recent Trends in Video Analysis: A Taxonomy of Video Classification Problems.

Abstract: This paper reviews and analyses the problems facing video classification. It investigates how the semantic gap can be bridged. It presents a new taxonomy for video calssifaction based on a lituture survey. It conclueds that narrowing the domain is the current approach to bridgeing the semantic gap.

Classification of Video Genre using Audio

Abstract: In this paper we propose an approach to high-level classification of video into genre: sport, cartoon, news, commercial and music. An important issue for automatic high-level classification systems is the amount of time needed to classify a video. Here we investigate classification performance as a function of the test sequence length. In addition we present performance against different orders and combinations of static and dynamic mel-frequency cepstral coefficients (MFCC). We find that static and delta MFCCs perform well for this classification task. A test sequence length of approximately 25 seconds for the 5 class problem gives approximately 80% correct identification.

Motion-based classification of cartoons

Abstract: This paper describes a simple high-level classification of multimedia broadcast material into cartoon non-cartoon The input video sequences are from a broad range of material which is representative of entertainment viewing Classification of this type of high-level video genre is difficult because of its large inter-class variation The task is made more difficult when classification is over a small time (10's of seconds) introducing a great deal of intra-class variation This paper presents a purely dynamic based approach for content-based classification of video sequences in the form of a new global motion measure of foreground objects Experiments are reported on a diverse database consisting of: 8 cartoon and 20 non-cartoon sequences Results are shown in identification error rates against time of sequence used for classification The system produces a best identification error rate of 3% on 66 separate decisions based on 23 second sequences trained using a total of /spl sim/20 minutes of video

A Survey on Visual Content-Based Video Indexing and Retrieval

Abstract: 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.

Automatic Video Classification: A Survey of the Literature

Abstract: There is much video available today. To help viewers find video of interest, work has begun on methods of automatic video classification. In this paper, we survey the video classification literature. We find that features are drawn from three modalities - text, audio, and visual - and that a large variety of combinations of features and classification have been explored. We describe the general features chosen and summarize the research in this area. We conclude with ideas for further research.

Video Question Answering via Gradually Refined Attention over Appearance and Motion

Abstract: Recently image question answering (ImageQA) has gained lots of attention in the research community. However, as its natural extension, video question answering (VideoQA) is less explored. Although both tasks look similar, VideoQA is more challenging mainly because of the complexity and diversity of videos. As such, simply extending the ImageQA methods to videos is insufficient and suboptimal. Particularly, working with the video needs to model its inherent temporal structure and analyze the diverse information it contains. In this paper, we consider exploiting the appearance and motion information resided in the video with a novel attention mechanism. More specifically, we propose an end-to-end model which gradually refines its attention over the appearance and motion features of the video using the question as guidance. The question is processed word by word until the model generates the final optimized attention. The weighted representation of the video, as well as other contextual information, are used to generate the answer. Extensive experiments show the advantages of our model compared to other baseline models. We also demonstrate the effectiveness of our model by analyzing the refined attention weights during the question answering procedure.

Transcatheter Valve Implantation for Patients with Aortic Stenosis : a position statement from the European Association of Cardio-Thoracic Surgery (EACTS) and the European Society of Cardiology (ESC), in collaboration with the European Association of Percutaneous Cardiovascular Interventions (EAPCI)

Abstract: Aims: To critically review the available transcatheter aortic valve implantation techniques and their results, as well as propose recommendations for their use and development. Methods and results: A committee of experts including European Association of Cardio-Thoracic Surgery and European Society of Cardiology representatives met to reach a consensus based on the analysis of the available data obtained with transcatheter aortic valve implantation and their own experience. The evidence suggests that this technique is feasible and provides haemodynamic and clinical improvement for up to 2 years in patients with severe symptomatic aortic stenosis at high risk or with contraindications for surgery. Questions remain mainly concerning safety and long-term durability, which have to be assessed. Surgeons and cardiologists working as a team should select candidates, perform the Procedure, and assess the results. Today, the use of this technique should be restricted to high-risk patients or those with contraindication for surgery. However, this may be extended to lower risk patients if the initial promise holds to be true after careful evaluation. Conclusion: Transcatheter aortic valve implantation is a promising technique, which may offer an alternative to conventional surgery for high-risk patients with aortic stenosis. Totiay, careful evaluation is needed to avoid the risk of uncontrolled diffusion.

Detecting content on a social network using links

Abstract: In one implementation, a computer-implemented method includes receiving at information related to users of a social network site, and determining a social network graph for at least a portion of the social network, the graph including a plurality of nodes connected by links, each node corresponding to a user that has a profile page on the social network. The method can also include identifying first nodes from the plurality of nodes as including content associated with a particular subject of interest, and seeding the identified first nodes with first scores. The method can additionally include determining second scores for second nodes based on propagation of the first scores from the first nodes to the second nodes using the links of the social network graph; and providing the determined second scores for the second nodes.