000			02730nam a22001577a 4500
082			_a629.8
100			_aALI, HASNAIN _9118496
245			_aMultimedia Analytics for Scene Content Understanding / _cHASNAIN ALI
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a133p. _bSoft Copy _c30cm
500			_aWith the rapid expansion of video content, understanding how humans retain and recall visual data has become crucial. Memorability, a key neurocognitive process, plays a significant role in retaining and retrieving video content. While past research has explored image memorability, video memorability has received less attention, leaving a gap in robust computational models for predicting memorable video events. This thesis addresses this gap through a multi-phase study focused on video memorability prediction, scalable feature extraction, and behavior training for robotic systems. The first study introduces a novel framework that predicts episodic video memorability by fusing deep features, including text, color, and motion. Episodic sequences are generated using a Fuzzy FastText model and color histogram analysis, while scene objects are identified using a Faster Region-based Convolutional Neural Network (Faster R-CNN). The fusion of these features results in improved short- and long-term memorability, with a superior Spearman’s rank correlation of 0.6428 and 0.4285, respectively. The second study focuses on a robust Stacked Bin-Convolutional Neural Network (SB-CNN) and Sparse Low-Rank Regressor (SLRR) model. This model improves video event classification by employing a low-rank representation technique that reduces noise in video frames, leading to more accurate predictions. The Multi-Attribute Decision Making (MADM) technique is applied to enhance decision making, achieving a recall time of 49.9247 on public datasets. In the final study, a Trimmed Q-learning algorithm is introduced to optimize memorability-driven scene prediction in mobile robots. The training is conducted through online, short-term, and long-term learning modules, with significant improvements in memorability scores: 72.84% for short-term and online learning, and 68.63% for long-term learning. By linking these phases, this thesis presents an integrated framework that effectively addresses the challenges of video memorability prediction, robust feature scaling, and robotic decision-making, offering practical insights for both academic research and real-world applications.
650			_aPhD Robotics and Intelligent Machine Engineering _9123222
700			_aSupervisor : Dr Syed Omer Gilani _9119645
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/56235
942			_2ddc _cTHE
999			_c615352 _d615352