Face Sketch Synthesis and Recognition Based on Linear Regression Transformation and Multi-Classifier Technique

Citation:
Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face Sketch Synthesis and Recognition Based on Linear Regression Transformation and Multi-Classifier Technique", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015.

Date Presented:

Nov. 28-30

Abstract:

Fish detection and identi cation are important steps towards
monitoring sh behavior. The importance of such monitoring step comes
from the need for better understanding of the sh ecology and issuing
conservative actions for keeping the safety of this vital food resource.
The recent advances in machine learning approaches allow many appli-
cations to easily analyze and detect a number of sh species. The main
competence between these approaches is based on two main detection
parameters: the time and the accuracy measurements. Therefore, this
paper proposes a sh detection approach based on BAT optimization
algorithm (BA). This approach aims to reduce the classi cation time
within the sh detection process. The performance of this system was
evaluated by a number of well-known machine learning classi ers, KNN,
ANN, and SVM. The approach was tested with 151 images to detect the
Nile Tilapia sh species and the results showed that k-NN can achieve
high accuracy 90%, with feature reduction ratio close to 61% along with
a noticeable decrease in the classi cation time.