  Automation of feature analysis in the dynamic image frame dataset deals with
complexity of intensity mapping with normal and abnormal class. The
threshold-based data clustering and feature analysis requires iterative model
to learn the component of image frame in multi-pattern for different image
frame data type. This paper proposed a novel model of feature analysis method
with the CNN based on Convoluted Pattern of Wavelet Transform (CPWT) feature
vectors that are optimized by Grey Wolf Optimization (GWO) algorithm.
Initially, the image frame gets normalized by applying median filter to the
image frame that reduce the noise and apply smoothening on it. From that, the
edge information represents the boundary region of bright spot in the image
frame. Neural network-based image frame classification performs repeated
learning of the feature with minimum training of dataset to cluster the image
frame pixels. Features of the filtered image frame was analyzed in different
pattern of feature extraction model based on the convoluted model of wavelet
transformation method. These features represent the different class of image
frame in spatial and textural pattern of it. Convolutional Neural Network (CNN)
classifier supports to analyze the features and classify the action label for
the image frame dataset. This process enhances the classification with minimum
number of training dataset. The performance of this proposed method can be
validated by comparing with traditional state-of-art methods.
