A time-stamped authoring graph for video databases

Lecture Notes in Computer Science - Database and Expert Systems Applications ◽

10.1007/bfb0022031 ◽

1997 ◽

pp. 192-201 ◽

Cited By ~ 5

Author(s):

Kohji Zettsu ◽

Kuniaki Uehara ◽

Katsumi Tanaka ◽

Nobuo Kimura

Keyword(s):

Video Databases

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A neural network approach to interactive content-based retrieval of video databases

Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348) ◽

10.1109/icip.1999.822866 ◽

1999 ◽

Cited By ~ 6

Author(s):

N.D. Doulamis ◽

A.D. Doulamis ◽

S.D. Kollias

Keyword(s):

Neural Network ◽

Network Approach ◽

Video Databases ◽

Neural Network Approach ◽

Content Based Retrieval

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Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

10.26686/wgtn.14411021.v1 ◽

2021 ◽

Author(s):

Harisu Abdullahi Shehu ◽

William Browne ◽

Hedwig Eisenbarth

Keyword(s):

Machine Learning ◽

Facial Expression ◽

Facial Expressions ◽

Classification Accuracy ◽

Video Frame ◽

Video Databases ◽

Machine Learning Classifiers ◽

Learning Classifiers ◽

Good Classification ◽

Sequential Voting

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.

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