From visual patterns to semantic description: A cognitive approach using artificial curiosity as the foundation

Authors:
Dominik Maximilián Ramík;Kurosh Madani;Christophe Sabourin
Affiliations:
-;-;-
Venue:
Pattern Recognition Letters
Year:
2013

Citing 14
Cited 0

A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Human-robot interaction: a survey

Foundations and Trends in Human-Computer Interaction
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding
Curious George: An attentive semantic robot

Robotics and Autonomous Systems
Flexible word meaning in embodied agents

Connection Science - Social Learning in Embodied Agents
Human-Robot Interaction in Concept Acquisition: a computational model

DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Toward interactive learning of object categories by a robot: A case study with container and non-container objects

DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Salient region detection and segmentation

ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Learning to Detect a Salient Object

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-level cognitive machine-learning based concept for human-like "artificial" walking: Application to autonomous stroll of humanoid robots

Neurocomputing
Hybrid Salient Object Extraction Approach with Automatic Estimation of Visual Attention Scale

SITIS '11 Proceedings of the 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems
Salient object detection using content-sensitive hypergraph representation and partitioning

Pattern Recognition

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this article, we present a cognitive system based on artificial curiosity for high-level knowledge acquisition from visual patterns. The curiosity (perceptual curiosity and epistemic curiosity) is realized through combining perceptual saliency detection and Machine-Learning based approaches. The learning is accomplished by autonomous observation of visual patterns and by interaction with an expert (a human tutor) detaining semantic knowledge about the detected visual patterns. Experimental results validating the deployment of the investigated system have been obtained on the basis of a humanoid robot acquiring visually knowledge from its surrounding environment interacting with a human tutor. We show that our cognitive system allows the humanoid robot to discover the surrounding world in which it evolves, to learn new knowledge about it and describe it using human-like (natural) utterances.