Fine Granularity Conceptual Model for Bilinearity Fusion Features and Learning Methods in Multilayer Feature Extraction
Satya Prakash Yadav, Mahaveer Singh Naruka, Prashant Upadhyay, Sushant Chamoli, Rajesh Pokhariyal
Abstract
This research presents a novel approach for fine granularity image analysis by combining bilinearity fusion features and learning methods. A depth convolutional network model, VGG16, is utilized to extract multilayer features from the fine granularity images. The proposed method involves the fusion of features extracted from VGG-16conv4_1, VGG-16conv4_2, and VGG-16conv4_3 using bilinear feature descriptors. The fused features are then fed into a softmax-based multi-class classifier to obtain classification results. The preprocessing phase involves data enhancement techniques such as subtracting image mean value, noise elimination, random cropping, and image level overturning. By leveraging the fusion of fine granularity image multilayer features, the proposed approach enhances classification precision even with only image-level classification information.