Abstract-In this paper a new model for user-centered video summarization is presented. Involvement of more than one expert in generating the final video summary should be regarded as the main use case for this algorithm. This approach consists of three major steps. First, the video frames are scored by a group of operators. Next, these assigned scores are averaged to produce a singular value for each frame and, lastly, the highest scored video frames alongside the corresponding audio and textual contents are extracted to be inserted into the summary. The effectiveness of this approach has been evaluated by comparing the video summaries generated by this system against the results from a number of automatic summarization tools that use different modalities for abstraction.
In this paper, we propose a methodology for generating users' tailored video abstracts. First, video frames are scored by a group of video experts (operators) according to audio, visual and textual content of the video. Then, a matrix that contains the relevancy scores of each video scene into a number of pre-defined categories is computed using Scaled Invariant Feature Transform (SIFT) features, which are computed pairwise for representative keyframes and delegate images from the training collection. Later, for profiling purposes, an end-user's interest levels towards those high-level visual concepts (categories) are captured in the form of a vector. As a result of combining these two groups of data, the user's priorities in regards to different video segments can be determined. In the next stage, the initial averaged scores of the frames are updated based on the end-users' generated profiles. Eventually, the highest scored video frames alongside the auditory and textual content are inserted into final digest. The effectiveness of this approach has been evaluated by comparing the video summaries generated by this system against the results from a number of automatic and semi-automatic summarization tools that use different modalities for abstraction.
In this paper an expert-based model for generation of personalized video summaries is suggested. The video frames are initially scored and annotated by multiple video experts. Thereafter, the scores for the video segments that have been assigned the higher priorities by end users will be upgraded. Considering the required summary length, the highest scored video frames will be inserted into a personalized final summary. For evaluation purposes, the video summaries generated by our system have been compared against the results from a number of automatic and semi-automatic summarization tools that use different modalities for abstraction.
In this paper, modeling and simulation of Turbogenerators has been presented using artificial neural networks. The training and testing of neural network was done by MATLAB 6.5.1 software in order to find the optimum values of weights and biases. To find the optimal neural structure, training of several structures with two layers and three layers with different number of neurons in each layer has been done. Moreover, the neural network qualified with the least amount of error was presented along with their related charts.
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