TY  - JOUR
T1  - Novel Technique using Optimal Salp Swarm Based Feature Fusion with Linear Multi k-SVM
Classifier on Moving Object Imaging
AU - Jemilda, G. AU - Baulkani, S. 
JO  - Asian Journal of Information Technology
VL  - 19
IS  - 1
SP  - 21
EP  - 27
PY  - 2020
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2020.21.27
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2020.21.27
KW  - image detection
KW  -Image segmentation
KW  -feature extraction
KW  -object classification
KW  -subtraction techniques
AB  - To develop a new automatic moving object
segmentation and classification system from the level-1
and level-2 sub bands, the Local Shape (LoS) and the
Histogram oriented Gradients (HoG) features are
extracted. These extracted features are then fused at the
feature level Fusion using Salp Swarm optimization
(FFSSO) algorithm. For convenience, the fused features
are now called w-LoSHoG descriptor hereafter.
Moreover, the feature extraction technique is applied on
Least Enclosing Rectangle (LER) of the segmented object
to increase the processing speed. The main intuition of
this salp swarm algorithm relays on reducing the
computational load of the proposed classifier by removing
the repetitive and unrelated features from the feature
vector. Also, increased training samples of similar shaped
classes when applied on the classifier can generate the mis
classification results. Thus, a new layered kernel based
Support Vector Machine (k-SVM) classifier is developed
by means of integrating the k-neural network classifier
and layered SVM classifier. Because of the high
dimensional features there occurs a difficulty in the
application of single classifier. In order to ease the
computational load, this multi classifier is integrated with
shadow elimination technique to classify the object
categories of intelligent transportation system such as
motorcycle, bicycle, car and pedestrians.
ER  -