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.
Keywords: Bilinearity fusion features, Bilinear feature descriptor, Classification, Conceptual model, Data preprocessing, Depth convolutional network, Fine granularity, Image analysis, Learning methods, Multilayer feature extraction, VGG16.
