Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding

Joint Authors

Ghozia, Ahmed
Attiya, Gamal
Adly, Emad
El-Fishawy, Nawal

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-12-24

Country of Publication

Egypt

No. of Pages

15

Main Subjects

Biology

Abstract EN

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 or surprised by a lovely gift or just playing in the backyard.

Artificial intelligence is a heterogeneous process that goes beyond learning.

In this article, 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 the 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 context-aware 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 real-time scenarios.

Moreover, we discuss the pros and cons of deep learning architectures.

American Psychological Association (APA)

Ghozia, Ahmed& Attiya, Gamal& Adly, Emad& El-Fishawy, Nawal. 2020. Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1138848

Modern Language Association (MLA)

Ghozia, Ahmed…[et al.]. Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1138848

American Medical Association (AMA)

Ghozia, Ahmed& Attiya, Gamal& Adly, Emad& El-Fishawy, Nawal. Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1138848

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1138848