Huge Video Databases Research Articles

Geometrically robust video hashing based on ST-PCT for video copy detection

Copy videos flooding in the network infringe the video copyright and heavy the storage pressure on the video services server, which raises a huge demand for video copy detection techniques that can accurately and quickly detect the copies of video from huge video database. This paper aims to generate a video hash which has not only high discrimination but also robustness against geometrical and spatial-temporal transformations. First, we propose Spatial-Temporal Polar Cosine Transform (ST-PCT), which considers a video as a three-dimensional matrix and performs two-dimensional Polar Cosine Transforms (PCT) after performing a one-dimensional Discrete Cosine Transform (DCT) on video. This transformation can extract features of the spatial-temporal domain and has geometric invariance. Then, based on ST-PCT, we propose a geometrically robust video hashing method for video copy detection. The video features generated by ST-PCT are compressed and quantified to a compact binary hash code. Experimental results show that compared with the state-of-the-art methods, the proposed method has better robustness, higher accuracy, and faster calculation speed.

A fast and effective method for static video summarization on compressed domain

Current advances in information technologies have led to the creation of huge video databases. Efficient and effective video summarization methods are needed to quickly browse and locate required video content. In this paper, we present a fast and effective video summarization method that is implemented in the compressed domain. Our four-step proposed method relies on a simple yet powerful descriptor and a scene-detection method, to detect gradual and abrupt transitions with great precision. A saliency-based refinement strategy is used to avoid redundancy and represent video content with as few key-frames as possible. Several experiments were done to assess the proposed method's performance, concluding that it is superior to current solutions.

Perceptual synoptic view-based video retrieval using metadata

Content-based video retrieval and video synopsis are generally considered as two different areas. In this paper, we present an efficient approach for video retrieval based on the perceptual synopsis database of the videos. Video synopsis encapsulates an overview of a shot in a single frame. This is the first time video synopsis is used for video indexing providing the user an intuitive link for accessing actions in the video. We propose an enhanced synopsis called meta synopsis for the video database index, which will contain all essential information for retrieval. Various information such as background of a scene, motion trajectory of the foreground objects, color, texture, and mutual information in the synopsis database will empower us to retrieve relevant video content from huge video databases. Experiments were conducted on the OVP, BBC Motion Gallery, TRECVID data set, and other videos. Instead of using key frames as the query frames, the method accepts any arbitrary query frames. The experimental results illustrate that our proposed method can accurately identify a pertinent video from huge video databases.

미국 방위산업체 상황실의 인물검색 활동을 돕는 가상현실 공간 인터페이스 환경에 관한 연구

본 연구는 빠른 속도로 발전하는 멀티미디어 정보 환경 중에서도 미국 방위산업체 정보기관의 감시 카메라 속 특정 인물을 색출하는 작업 상황을 가정으로, 사용자가 방대한 영상 데이터 기록물들 가운데 원하는 정보를 한 눈에 분석하고 검색해 낼 수 있는 효율적이고 만족스러운 멀티미디어 검색 환경에 대해 연구하였다. 이를 위해 첫 번째 실행된 실험에서 CAVE형 가상현실 공간 인터페이스를 검색환경으로 제안하고 데스크톱과 CAVE형 공간 인터페이스를 비교 평가하였다. 이 실험에 사용한 콘텐츠는 각 과제에 최적화되어 개발된 소프트웨어들과 데이터베이스를 사용하였다. 두 번째 실험은 CAVE형 가상현실 공간 인터페이스에서 검색의 효율을 높여주는 입력시스템에 대한 연구를 실행하였다. 특히, 인간의 움직임에 보다 가깝게 디자인되어 자연스럽게 조작할 수 있도록 설계된 제스처 입력시스템을 중심으로 CAVE형 가상현실 인터페이스에 적합한 3가지 입력시스템을 사용하여 검색 후 각 과제들에 대한 선호도와 만족도를 측정하였다. 또한 각 과제별 검색 수행 시간을 측정하여 효율성 높은 입력시스템을 분석하였다.

Video Content Indexing using Kullback-Leibler Distance

In huge video databases, the effective video content indexing method is required. While manual indexing is the most effective approach to this goal, it is slow and expensive. Thus automatic indexing is desirable and recently various indexing tools for video databases have been developed. For efficient video content indexing, the similarity measure is an important factor. This paper presents new similarity measures between frames and proposes a new algorithm to index video content using Kullback-Leibler distance defined between two histograms. Experimental results show that the proposed algorithm using Kullback-Leibler distance gives remarkable high accuracy ratios compared with several conventional algorithms to index video content.