Intelligence across humans and machines: a joint perspective

This paper aims to address the divergences and contradictions in the definition of intelligence across different areas of knowledge, particularly in computational intelligence and psychology, where the concept is of significant interest. Despite the differences in motivation and approach, both fields have contributed to the rise of cognitive science. However, the lack of a standardized definition, empirical evidence, or measurement strategy for intelligence is a hindrance to cross-fertilization between these areas, particularly for semantic-based applications. This paper seeks to equalize the definitions of intelligence from the perspectives of computational intelligence and psychology, and offer an overview of the methods used to measure intelligence. We argue that there is no consensus for intelligence, and the term is interchangeably used with similar, opposed, or even contradictory definitions in many fields. This paper concludes with a summary of its central considerations and contributions, where we state intelligence is an agent's ability to process external and internal information to find an optimum adaptation (decision-making) to the environment according to its ontology and then decode this information as an output action

Evaluation metrics for video captioning: A survey

Automatic evaluation metrics play an important role in assessing video captioning systems. Popular metrics used for assessing such approaches are based on word matching and may fail to evaluate the quality of automatically generated captions due to inherent natural language ambiguity. Moreover, they require many reference sentences for effective scoring. With the fast development of image and video captioning methodologies using deep learning in recent years, many metrics have been proposed for evaluating such approaches. In this study, we present a survey of automatic evaluation metrics for the video captioning task. Moreover, we highlight the challenges in evaluating video captioning and propose a taxonomy to organize the existing evaluation metrics. We also briefly describe and identify the advantages and shortcomings of those metrics and identify applications or contexts in which these metrics can be better used. To identify the advantages and limitations of the evaluation metrics, we quantitatively compare them using videos from different datasets employed for the video description task. Finally, we discuss the advantages and limitations of the metrics and propose some promising future research directions, such as semantic measurement, explainability, adaptability, extension to other languages, dataset limitations, and multimodal free-reference metrics

Global, semi-global and local color angular features for unsupervised face recognition

In face recognition applications, dealing with images under different conditions is a challenging task because they can affect dramatically the recognition performance. Among many image features, color is an useful feature which is generally used for image matching and retrieval purposes. Besides, to represent images through features, we generally need an extensive number of parameters forming a large feature set. Color angles need only three parameters to represent an image in a small feature set and are considered as pose and illuminant-invariant. Hence, in this work, we have made an attempt to study the use of color angles in face recognition approach with images obtained under different conditions. In addition to this, face image features are spatially extracted from different combination of sub-images similar to the edge histogram descriptor scheme denominated as Global, Semi-Global and Local features. Since we have proposed an unsupervised learning approach, no previous knowledge about images are required. Six types of images obtained under two different illumination conditions including with face expression and scale are used as query images in a base of images obtained under controlled condition. According to the experimental results, an expressive recognition rate can be obtained from face expression and scale. One of the main goal of this work is the use of Semi-Global features with Global and Local features. From this initial study, we can identify that the Local and Semi-Global features influence in the recognition performance than Global features.500