الفهرس 
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Abstract
Understanding video files is a challenging task. While the current video
understanding techniques rely on deep learning, the obtained results suffer from a
lack of real trustful meaning. Deep learning recognizes patterns from big data,
leading to deep feature abstraction, not deep understanding. Deep Learning tries to
understand multimedia production by analyzing its content. We cannot understand
the semantics of a multimedia file by analyzing its content only. Events occurring in
a scene earn their meanings from the context containing them. A screaming kid could
be scared of a threat, surprised by a lovely gift or just playing in the backyard.
Artificial intelligence is a heterogeneous process that goes beyond learning. In this
thesis, we discuss the heterogeneity of AI as a process that includes innate
knowledge, approximations, and context awareness. We present a context-aware
video understanding technique that makes the machine intelligent enough to
understand the message behind the video stream. The main purpose is to understand
video stream by extracting real meaningful concepts, emotions, temporal data, and
spatial data from the video context. The diffusion of heterogeneous data patterns
from the video context leads to accurate decision making about the video message
and outperforms systems that rely on deep learning. Objective and subjective
comparisons prove the accuracy of the concepts extracted by the proposed contextaware technique in comparison with the current deep learning video understanding
techniques. Both systems are compared in terms of retrieval time, computing time,
data size consumption, and complexity analysis. Comparisons show a significant
efficient resource usage of the proposed context-aware system, which makes it a
suitable solution for realtime scenarios. Moreover, we discuss the contra and pro of
deep learning architectures.