Research On Key Issues In Video Information Content Management

With the rapid developments of network communication and multimedia technologies, there has been an explosive growth on the amount of video data. At the same time, video oriented applications keep emerging in recent years, such as Internet TV, 3G video communication, video on demand (VOD) and video sharing. Consequently, the vast amount of video information and the extension of application scenarios have lead to significant changes on the ways of video acquisition, utilization and distribution. The conventional monotonous and passive video acquisition modes are being replaced by diverse and interactive multimedia services. Meanwhile, the ever increasing video information also results in a series of technological and social problems. There has been a strong demand of video organizing, utilizing, copyright management and content authentication techniques. However, the conventional indexing, retrieval and information security techniques cannot be simply extended to video information. Therefore, developing efficient and effective video information management techniques has become a major topic of interest in both academia and the multimedia industry.Taking the characteristics of video information as the point of departure, this dissertation addresses the technical issues arising from video applications. The principal goal of this dissertation is to design effective content management schemes to enhance the availability and reliability of video information. The research work of this dissertation focuses on video structure parsing, video abstraction, video identification and content authentication.The main work and contributions of this dissertation are as follows:A fast shot boundary detection framework that employs pre-processing techniques is proposed. The motivation of our work is not to design a specific hard cut or gradual transition (GT) detection method. Instead, we concentrate on a fast shot boundary detection framework that can enhance the efficiency of shot boundary detection. Several pre-processing techniques are incorporated in the framework to eliminate non-boundary frames and predict the attributes of potential shot boundaries. Moreover, we also propose a fast shot boundary detection paradigm that is parallel with video coding. The side information generated by video encoder is exploited to facilitate shot boundary detection. As a result, the detector can get rid of the computationally intensive feature extraction procedure. Experimental results indicate that both of the proposed works can effectively improve the efficiency of shot boundary detection, while the detection accuracy can be maintained at a satisfactory level.In order to facilitate video browsing, an attention model and on-line clustering based video abstraction algorithm is developed. We first investigate the visual neuron-physiology mechanisms of human attention, based on which the visual attention model is employed in video abstraction. Region of interests (ROI) are detected in each representative frame by simulating the functions of human visual system components in forming attention. In order to reduce the consumption of memory and achieve on-the-fly key frame representation, an on-line clustering scheme is proposed. It is revealed in simulation that the proposed key frame extraction algorithm is content adaptive, and the extracted key frames are well consistent with the results of human perceptions.We also present a spatial-temporal salient points based video identification algorithm. The spatial-temporal salient points are detected with the aid of the Harris detector and trajectory tracking techniques. The stability of each salient point is evaluated from both spatial and temporal aspects, and those with the highest spatial saliency and temporal stability are selected as the feature for video identification. In order to cope with the arbitrary order of salient points, the Hausdorff distance is employed as the metric for feature comparison. In addition, a non-negative matrix factorization (NMF) based video identification algorithm is proposed. The updating function of NMF under the Euclidean norm criterion is derived in this work. Consequently, NMF is performed on the input video to obtain the basis images that can represent the spatial-temporal content essence of the input video. Video sequences are identified via the features of basis images. It is demonstrated that the proposed algorithms can achieve accurate video identifications, and their performances are superior to that of the state-of-the-art algorithm. Especially, the spatial-temporal salient points can effectively resist geometrical distortions.Also, the application of robust hashing in content authentication is investigated in this work. Firstly, the extension of the concept of hash function from generic data to multimedia data is elaborated. We propose a random Gabor filtering and dithered lattice vector quantization (DLVQ) based robust hash function. In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant. Consequently, a key dependent random filtering scheme is developed to facilitate secure feature extraction. The relationship between the security and randomness of robust hash function is investigated. Consider the limitations of existing quantization schemes, a DLVQ based quantization scheme is developed. The efficiency of the DLVQ based quantization scheme is illustrated by analytical and experimental results. It has been revealed that the proposed robust hashing performs outstandingly well on robustness and discrimination. Especially, it shows significant advantages over state-of-the-art algorithms on the robustness against rotation manipulations. In addition, a spatial-temporal energy based video hashing algorithm is developed. The energy relationships between different regions are calculated using three dimensional signal transform and random block partition. The proposed work outperforms existing works in terms of robustness. In addition, analytical results show that the proposed video hashing algorithm can exhibit a high amount of randomness.