This paper presents a new method for remote and interactive browsing of long video surveillance sequences. The solution is based on interactive navigation in JPEG 2000 coded mega-images. We assume that the video 'key-frames' are available through automatic detection of scene changes or abnormal behaviors. These key-frames are concatenated in raster scanning order forming a very large 2D image, which is then compressed with JPEG 2000 to produce a scalable video summary of the sequence. We then exploit a mega image navigation platform, designed in full compliance with JPEG 2000 part 9 "JPIP", to search and visualize desirable content, based on client requests. The flexibility offered by JPEG 2000 allows highlighting key-frames corresponding to the required content within a low quality and low-resolution version of the whole summary. Such a fine grain scalability is a unique feature of our proposed JPEG 2000 video summaries expansion. This possibility to visualize key-frames of interests and playback the corresponding video shots within the context of the whole sequence enables the user to understand the temporal relations between semantically similar events. It is then particularly suited to analyzing complex incidents consisting of many successive events spread over a long period.
This paper describes a video summarization and semantics editing tool that is suited for content-based video indexing and retrieval with appropriate human operator assistance. The whole system has been designed with a clear focus on the extraction and exploitation of motion information inherent in the dynamic video scene. The dominant motion information has ben used explicitly for shot boundary detection, camera motion characterization, visual content variations description, and for key frame extraction. Various contributions have been made to ensure that the system works robustly with complex scenes and across different media types. A window-based graphical user interface has been designed to make the task very easy for interactive analysis and editing of semantic events and episode where appropriate.
One of the major problems in supervised learning of neural networks is the inevitable local minima inherent in the cost function f(W,D). This often makes classic gradient-descent-based learning algorithms that calculate the weight updates for each iteration according to (Delta) W(t) equals -(eta) (DOT)$DELwf(W,D) powerless. In this paper we describe a new strategy to solve this problem, which, adaptively, changes the learning rate and manipulates the gradient estimator simultaneously. The idea is to implicitly convert the local- minima-laden cost function f((DOT)) into a sequence of its smoothed versions {f(betat)}Ttequals1, which, subject to the parameter (beta) t, bears less details at time t equals 1 and gradually more later on, the learning is actually performed on this sequence of functionals. The corresponding smoothed global minima obtained in this way, {Wt}Ttequals1, thus progressively approximate W--the desired global minimum. Experimental results on a nonconvex function minimization problem and a typical neural network learning task are given, analyses and discussions of some important issues are provided.
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