TY  - BOOK
AU  - ALI, HASNAIN 
AU  - Supervisor : Dr Syed Omer Gilani
TI  - Multimedia Analytics for Scene Content Understanding
U1  - 629.8 
PY  - 2025///
CY  - Islamabad : 
PB  - SMME- NUST; 
KW  - PhD Robotics and Intelligent Machine Engineering
N1  - With 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
UR  - http://10.250.8.41:8080/xmlui/handle/123456789/56235
ER  -