Semantic-based Video Retrieval Research Articles

The Algorithm of Scene Summary synthesis Based on Semantics

According to the key technique of semantic-based video retrieval, the paper presents directly extracting I-frames in the compressed domain, and using the DC coefficient of I frame to establish the model of information system. Then the reduction of I Frame by the theory of attribute reduction based on RS, to produce A key frame that reflects the main content of the lens. Finally, the key frames ‘s DC coefficient has constructed the mathematical modeling, and has classified the shot according to the differences of frames. So the polymerization of I frames of similar lens, we could obtain the scenario summary.

The Algorithm of Scenario Summary Synthesis Based on Semantics

The MPEG video data contains three types of frames, which are I frame, P frame and B frame. However, the I frame records the main information of video data, and the P frame and B frame are just as motion compensation of the I frame. The paper presents the method that analyzes the MPEG video stream in the compressed domain, and then according to the key technique of semantic-based video retrieval the paper presents directly extracting I-frames in the compressed domain, and using the DC coefficient of I frame to establish the model of information system. Then reduce I Frame by the theory of attribute reduction based on RS, and produce a key frame that reflects the main content of the shots. Finally, the key frame’s DC coefficient has constructed the mathematical modeling, and has classified the shot according to the differences of frames. So by the polymerization of I frames of similar shots, we could obtain the scenario summary. Experiment indicated that this method can be realized in the compressed MPEG video automatically, and can lay the foundation for the video processing in the future.

Hidden conditional random field-based soccer video events detection

Detect highlight event is an important step for semantic-based video retrieval. Hidden conditional random field (HCRF) is a discriminative model, which is effective in fusing observations for event inference. Mid-level semantics and their refinements are more robust than low-level visual features in event detection for learning models. To make full use of the contextual information, two aspects are taken into account during soccer video event detection. The first is parsing video sequences into event clips. The second is fusing the temporal transitions of the mid-level semantics of an event clip to determine the event type. In this study, HCRF is utilised to model the observations of mid-level semantics of an event clip for event detection. Comparisons are made with the dynamic Bayesian networks, hidden Markov model (HMM), enhanced HMM and conditional random field-based event detection approaches. Experimental results show the effectiveness of the proposed method.

Semantic Representation of Motion for Tracing Object in Surveillance System

Recent proliferation of multimedia data necessitates effective and efficient methods of retrieving of multimedia data. Especially, intelligent surveillance system needs semantic multimedia data processing. However, these studies are significantly insufficient to enable a complete semantic recognition in regard to semantic-based video retrieval. Current technology focuses on analysis of low-level characteristics such as color, texture, shape, and trajectory of an object and on applications of those characteristics. In this paper, we develop a semantic representation in human language to reduce the semantic gap between low-level and high-level characteristics; with consideration of not only the low-level characteristics but also the high-level characteristics with the use of the human language. Particularly, this paper focuses on semantic representation by using topological and directional relations between non-moving and moving objects for intrusion detection system using Closed-Circuit Television (CCTV). To this end, we capitalize on size relation of the object based on the previous studies to suggest 3D spatio-temporal relation. We also intend to reduce semantic gap by mapping with the use of predicates. This paper extends our previous work [17] published on SeCIHD 2011.

자동 주석 및 히스토그램 기법을 이용한 환경 교육 컨텐츠 검색 시스템

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic- based retrieval method can be available for various query of users. In this paper, we propose semantic-based video retrieval system for Environment Education Contents which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user`s fundamental query and selection of image for key frame that extracted form query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

Semantic-Based Surveillance Video Retrieval

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.

의미기반 비디오 검색을 위한 인덱싱 에이전트의 설계

최근 멀티미디어 정보의 양이 매우 빠른 속도로 증가함에 따라 비디오 데이터에 대한 다양한 검색은 더욱 중요한 의미를 가지게 되었다. 비디오 데이터를 효율적으로 처리하기 위해서는 비디오 데이터가 가지고 있는 내용에 대한 정보를 데이터베이스에 저장하고 사용자들의 다양한 질의를 처리할 수 있는 의미기반 검색 기법이 요구된다. 기존의 내용기반 비디오 검색 시스템들은 주석기반 검색 또는 특징기반 검색과 같은 단일 방식으로만 검색을 하므로 검색 효율이 낮을 뿐 아니라 완전한 자동 처리가 되지 않아 시스템 관리자나 주석자의 많은 노력을 요구한다. 본 논문에서는 주석기반 검색과 특징기반 검색을 이용하여 대용량의 비디오 데이터에 대한 사용자의 다양한 의미검색을 지원하는 에이전트 기반에서의 자동화되고 통합된 비디오 의미기반 검색 시스템을 제안한다. 사용자의 기본적인 질의와 질의에 의해 추출된 키 프레임의 이미지를 선택함으로써 에이전트는 추출된 키 프레임의 주석에 대한 의미를 더욱 구체화시킨다. 또한, 사용자에 의해 선택된 키 프레임은 질의 이미지가 되어 제안하는 특징기반 검색기법을 통해 가장 유사한 키 프레임을 검색한다. 따라서 의미기반 검색을 통해 비디오 데이터의 검색의 효율을 높일 수 있도록 시스템을 설계한다. According to the rapid increase of multimedia data quantity recently, various means of video data search has been desired. In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency and requires many efforts of system administrator or annotator form less perfect automatic processing. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. Therefore, we design the system that can heighten retrieval efficiency of video data through semantic-based retrieval.