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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila

Khan, Faraz, Ahmed, Bouridane, Richard, Jiang, Tiancheng, Xia, Paul, Chazot and Abdel, Ennaceur (2019) Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila. In: UNSPECIFIED.

Item Type: Conference or Workshop Item (Paper)


Gene expression of social actions in Drosophilae has been attracting wide interest from biologists, medical scientists and psychologists. Gene-edited Drosophilae have been used as a test platform for experimental investigation. For example, Parkinson's genes can be embedded into a group of newly bred Drosophilae for research purpose. However, human observation of numerous tiny Drosophilae for a long term is an arduous work, and the dependence on human's acute perception is highly unreliable. As a result, an automated system of social action detection using machine learning has been highly demanded. In this study, we propose to automate the detection and classification of two innate aggressive actions demonstrated by Drosophilae. Robust keypoint detection is achieved using selective spatio-temporal interest points (sSTIP) which are then described using the 3D Scale Invariant Feature Transform (3D-SIFT) descriptors. Dimensionality reduction is performed using Spectral Regression Kernel Discriminant Analysis (SR-KDA) and classification is done using the nearest centre rule. The classification accuracy shown demonstrates the feasibility of the proposed system.

2019 Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila.pdf - Published Version

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Depositing User: Abdelkader Ennaceur


Item ID: 11417
Identification Number: arXiv:1909.04974
Official URL:

Users with ORCIDS

ORCID for Chazot Paul: ORCID iD
ORCID for Ennaceur Abdel: ORCID iD

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Date Deposited: 06 Jan 2020 11:37
Last Modified: 30 Sep 2020 10:46