Approche d’apprentissage pour l’analyse des Big Data
| dc.contributor.author | Benoughidene Abdelhalim | |
| dc.date.accessioned | 2024-05-14T10:15:56Z | |
| dc.date.available | 2024-05-14T10:15:56Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The development of information technology has led to the big data revolution, with the amount of data produced increasing at a high rate. Video data is a significant component of big data, and the concept of automatically analyzing this rapidly increasing video content has become a popular research topic. In such a scenario, video automatic analysis uses a new generation of information technologies, such as artificial intelligence (machine learning), which help transform traditional video analysis to be more efficient and convenient. Video summary (VS) is now one of the primary areas of study in video analysis. Despite the use of big data-driven models, producing accurate video summaries in an efficient and effective manner remains a challenging task. The most effective and efficient way to transform lengthy, unstructured videos into structured, condensed, understandable, and useful information is through the use of video summaries. The primary goal of summarizing a video is to break it down into shots and select key frames from each shot that best capture the essence and flow of the entire video. The goal of this thesis is to improve the performance of video summarization systems by enhancing the quality of analytical techniques. To achieve this goal, we proposed two contributions. First, we suggested a shot boundary detection (SBD) method to adapt key shots and exploit its potential in video summaries. This is the first step in the video summarization process, and the results have a significant impact on the quality of the final summary. The main idea behind SBD is to extract features from video frames and then identify the boundaries between shots based on the differences in the features. Second, we focused on improving video summaries using unsupervised machine learning techniques (DBSCAN) and genetic algorithms (GA) to optimize DBSCAN hyperparameters. We validated the proposed methods and results obtained through extensive comparative analysis using datasets. | |
| dc.identifier.uri | https://dspace.univ-batna2.dz/handle/123456789/1745 | |
| dc.language.iso | fr | |
| dc.title | Approche d’apprentissage pour l’analyse des Big Data |