In this article, we propose a new approach to scene segmentation of video based on the shot detection. The methods of segmentation by scenes are still expensive in computation, in most of time; this detection uses matrices of dissimilarity as calculation kernel for clustering algorithms, which implies a quadratic nature of these calculations. However, shot segmentation algorithms are generally linear complexity. To take advantage of the benefits of both methods, our idea is to apply two-level mixing of these kinds of segmentation algorithms. For this, we define a measure of dissimilarity characterized by points of interest determined from the frames of the video. The signature based on these points allows us to consider the sequence as a time series, which we try to model the behavior by a measure of dissimilarity. Our segmentation method is to cut the video by shot, to set the intervals of the shots and then retrieve the representative images (or key frames) of these shots. Last time we apply an automatized clustering algorithm on this set of keyframes to join the plans represented by similar key frames. The latter operation will group the shots of similar contents, but distributed in the video, on other words, the joint of the same contents shots constitutes a scene. Some experimental results on real sequences show the validity of our approach.Keywords: characterizations, key-frames, measure of dissimilarity (similarity), points of interest, non-supervised clustering, video segmentation in shots, video segmentation in scenes.
<span lang="EN-US">Hidden </span><span lang="IN">M</span><span lang="EN-US">arkov models (HMMs) are one of machine learning algorithms which have been widely used and demonstrated their efficiency in many conventional applications. This paper proposes a modified posterior decoding algorithm to solve hidden Markov models decoding problem based on MapReduce paradigm and spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective of this work is to improve the performances of HMM to deal with big data challenges. The proposed algorithm shows a great improvement in reducing time complexity and provides good results in terms of running time, speedup, and parallelization efficiency for a large amount of data, i.e., large states number and large sequences number.</span>
Hidden Markov Models are widely used for time continuous problems modelling and prediction. This paper presents two new improved algorithms for Gaussian continuous and mixture of Gaussian continuous Hidden Markov Models cases for solving learning problem for large scale multidimensional data. The design of our parallel distributed algorithms is based on Spark, the Big Data framework, thereby we can distribute data over several nodes through Resilient Distributed Datasets which allow to apply, in parallel, a set of operations. The proposed algorithms have two main advantages: a high computational time efficiency and a high scalability as well as an easy integration in Big Data frameworks. The complexity comparison results show great improvements in computational complexity (by a factor of (states number) 2) and execution time. Moreover, the new algorithms might be more effective by reducing the communication costs between the elements of the system involved in the learning task.
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