Abstract:
The problem of flowering plants recognition is considered as a challenging
problem and it has been partially solved under a certain condition that the
flowers be pre-detected and nearly optimal segmented. In this thesis, we
study the flower detection and recognition approaches and we demonstrate
the ability to detect flowers in full scene images by means of machine learning.
State of the art feature extraction methods are considered for the detection problems including: Discrete Wavelet Transform, Histogram of Oriented
Gradients and Gabor Filter. We consider the flowering plant detection and
recognition as classification problems. A set of Linear Support Vector Machines is used for classification. Poslets as a detection approach is considered
for flowering plants detection.
For training the classifiers to recognize a flower, we use a set of benchmark
images, the classifiers run over a natural test images using a multi-scale
scanning window to find the strong activations. For full scene detection,
We have built a new testing dataset of flowering plant species that include
10 flowering plant species. The experiments show that Discrete Wavelet
Transform features as input for linear Support Vector Machine is superior in
the performance of other features.
We have achieved an accuracy rate in detecting 10 flower species reaches
about 88%. Experimental results using Receiver Operating Characteristics
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