Ryoma Oami

Video material archive system for efficient video editing based on media identification

This paper proposes a new media identification related application; a video material archive system for efficient video editing. This system allows content creators to track the usage history of all the archived materials to support their access to the desired materials. It also provides the citation frequency of each material so that creators can measure its importance. A feature-based media identification method is employed to create the usage history and the citation frequency data. Experimental results show that the use of this identification method makes it possible to create those data fully automatically without forcing any changes to the existing devices

The MPEG-7 Video Signature Tools for Content Identification

This paper presents the core technologies of the video signature tools recently standardized by ISO/IEC Moving Picture Experts Group (MPEG) as an amendment to the MPEG-7 Standard (ISO/IEC 15938). The video signature is a high-performance content fingerprint that is suitable for desktop scale to web-scale deployment and provides high levels of robustness to common video editing operations and high temporal localization accuracy at extremely low false alarm rates, achieving a detection rate in the order of 96% at a false alarm rate in the order of five false matches per million comparisons. The applications of the video signature are numerous and include rights management and monetization, distribution management, usage monitoring, metadata association, and corporate or personal database management. In this paper, we review the prior work in the field, explain the standardization process and status, and provide details and evaluation results for the video signature tools.

Understanding and modeling user interests in consumer videos

The paper analyzes the interests of users in viewing and organizing consumer videos. It proposes a taxonomy of relevant concepts with three basic dimensions of interests (DOIs) and effective models to predict the user interests in each dimension. The three DOIs correspond to the objects, the scenes and the events. Our conclusions are backed with an extensive study, in which users were asked to annotate and score the importance of each DOI in short clips of diverse and real consumer videos. Analysis of the user study data reveals high consistency (70%) of the scores across different users and independence between objects and events. In addition, we show how heuristic rules and neural networks can accurately predict these scores using camera motion, foreground object and audio information. The automatic and effective prediction of user interests has the potential for improving applications for annotating and summarizing consumer videos

Efficient lossless video coding compatible with MPEG-2

This paper proposes a DCT-based lossless video coding scheme that has compatibility with MPEG-2. It is obtained by modifying the DCT and the quantization process in MPEG-2, and it outperforms lossless coding based on an ordinary DCT by 20% in terms of the compression ratio. In addition, the compressed data can be decoded not only with the corresponding decoding algorithm but also with MPEG-2. The images decoded with MPEG-2 retain high quality and a PSNR that exceeds 40 dB. This lossless coding scheme is suitable for applications where contribution quality is required, such as inter-studio transmission and video cassette recording in studios.

Visual duplicate based topic linking using a robust video signature

This paper proposes a topic linking using a robust video signature to detect visual duplicates for grouping coherent topics in a video archive. The proposed video signature, which was accepted as part of a new ISO/IEC standard “MPEG-7 Video Signature Tools”, is designed for robust and high-speed detection of visual duplicates in a large database. It represents intensity differences between various sub-regions in a frame, which provides robustness to various modifications to videos, including caption overlay and compression. We show that the proposed video signature significantly improves the detection rate of visual duplicate segments, by more than 40% under caption overlay, compared with conventional visual features. We also present our topic linking system with its visual presentation of topic groups for efficient browsing and viewing of videos.

Mobile Multimedia Library: an MPEG-7 application with camera-equipped mobile phones

This paper proposes a Mobile Multimedia Library (MML), a new application of information retrieval using camera-equipped mobile phones. MML allows users to get information of an unknown object anywhere and anytime. A user simply takes a picture of the object by a mobile phone and sends it directly to an MML server on which the picture is analyzed to identify the object. The MML server returns the identification result to the mobile phone and the user can browse information of the object on the display of the mobile phone. This application employs the k-Nearest Neighbor approach using eight MPEG-7 visual features to identify objects. A prototype system for animal identification has been developed to demonstrate effectiveness of the MML framework. It takes about ten seconds on average for the identification in which 63% and 79% queries were correctly identified within first four and first ten candidates, respectively, out of 229 categories.

Background scene classification robust to the influence of human regions

We propose a background scene classification method robust to the influence of human regions. Conventional methods classify scene of an image by using image features extracted from entire region in the image. Therefore, in these methods, the influence of the human region such as color of the skin and the clothes reduces classification accuracy of the background scene. Our method classifies background scene of an image by using image features extracted from only background region except detected human regions. The experimental results show that the proposed method improves average of the rate at the balance point between recall rate and precision rate in almost all background scenes compared to the conventional method.

Credit-title detection of video contents based on estimation of superimposed region using character density distribution

We propose a credit-title detection method of video contents based on estimation of superimposed region using character density distribution. Copyright information of video contents is manually extracted for the secondary use of those contents, and its cost is highly expensive. Therefore, automatic detection of credit titles that contain copyright information is highly demanded. However, accuracy of conventional methods is usually insufficient for this purpose. Our method first estimates credit-title-superimposed region based on character density distribution calculated in advance by using many video contents. Then, credit titles are detected in the estimated region. The experiment results show that proposed method improves both recall and precision rates compared to a conventional method. Furthermore, the processing time of the proposed method is less than half that of the conventional method for all contents.