Research Of Key Technologies On Content Based Video Retrieval And Indexing For Scenery Documentary

With the advance of technology of computer, telecommunication, wide-band network and compression of video and audio, digital video is widely coming into the life of people. It is becoming the emergent and important issues for efficient organization, representation, store and management of video data as well as rapid retrieval and browsing of video data. The traditional data management and retrieval method cannot do better to search the requiring information from the huge amount of video data because of the diversity, complexity of structure of video data as well as temporospatial and multi-dimensional structure of video data. As a result the content-based video retrieval system emerges, as the times require.The key technologies of content-based video retrieval and indexing for scenery documentary are researched in this paper and some novel algorithms are presented. The main contents of this paper are as following:This paper presents an algorithm for shot boundary detection based on SVM (support vector machine) in compressed domain. It uses the features, such as the type of macroblock, the difference between DC coefficients of two co-located blocks in successive frames and the type of frame, to segment a video into the shots by classifying the frames into three classes, namely, the frames of cut change, gradual change and non-change. No thresholds, which are often hard to select in most shot detection methods, are involved in our algorithm. The results of experiment have shown that this algorithm is robust for motion of camera and large objects in video, and has better precision of shot boundary detection compared with the classic double-threshold method, fuzzy k-mean cluster, k-mean cluster and the boundary detecion method based the types of macro-block and DCT coefficient.A multi-class support vector machine (SVM) is used to classify the video key frames of scenery documentary. The color histogram features and MPEG-7 edge histogram features from the video key frames of scenery documentary are combined to classify the key frame in SVM in order to obtain the semantics of the key frames. The performances of SVMs with difference kernel functions have been tested and compared.Constructing the video summarization in semantic level is very important. An algorithm of video summarization based on support vector machine (SVM) in semantic level is presented for the natural scenery documentary. At first, the shot keyframes are classified by SVM. Then the frames constructing the video summarization are selected from the shot keyframes of every class by the importance function we introduce. The scalable video summarization from coarse to fine level of detail can be achieved by changing the threshold of important function. The experiment results indicate that the proposed algorithm performs satisfactorily.Most algorithms of scene segmentation are based on the key frames of shots. This paper presents an algorithm of scene segmentation based on shots directly. First, a method is introduced to extract the features of histogram of color and texture of shot in compressed domain and then these features are used to cluster the shots by shot similarity to construct the scene in the video clips. An adaptive threshold is computed for different video contents and types. Experiments are presented with promising results on several scenery documentary and story movies.The scene is a high-level temporal video segment. This paper presents a method of scene segmentation based on semantic. At first, the semantics of shot key frames are extracted based on the color and the texture of key frames using the SVM (support vector machine). The semantic concept vectors of shot key frames are formed. The shot key frames are clustered by the semantic concept vectors to construct the video scene. The shot select function is defined to extract the scene key frame based on the value of shot select function. The experimental results are shown that the recall and the precision of our algorithm are better than those of A. Hanjalic's method.Video data modeling is an important issue for content-based retrieval. In this paper, we propose a semantic-based four-layer video data model for scenery documentary and our discussions focus on the extracting and representation of semantics of video data. The support vector machine (SVM) is used to bridge the gap between low-level visual features and high-level semantic concepts. Semantic concept vectors are defined to represent the semantics of shot and scene. The retrieval based on our video data model can be achieved at low-level feature layer and high-level semantic concept layer. The results of retrieval at difference layers can be integrated to improve the performance of retrieval.An algorithm of video retrieval based on relevance feedback is presented. It implements the automatic relevance feedback using the neural network and minimizes user interactions. The architecture of the neural network and the strategies of the retrieval have been instructed. The results of experiment have been given and compared with key frame-based video retrieval (KFVR). The precision of our algorithm is higher than KFVR about 6%.Shot is the basic unit of a video. This paper presents a method of computation of texture histogram of a shot and video retrieval based on shot in compressed domain. At first, the histograms of color and texture are extracted in all DC pictures of I frames in a shot, and then the alpha-trimmed average histograms (ATAH) of color and texture are computed. The ATAHs are used in Video retrieval with difference distance measures at shot level. The results of experiment show that the retrievals based on shot have good performance in distance measures of L1 and x~2 and the performance of retrieval based on shot is better than that based on key frame because the camera motion may result in inappropriate selection of key frame and influence the precision of retrieval of key frame.