Deep Learning Using Isotroping, Laplacing, Eigenvalues Interpolative Binding, and Convolved Determinants with Normed...

Area of Science:

• Computer Vision
• Machine Learning
• Deep Learning

Background:

• Convolutional Neural Networks (CNNs) excel at analyzing grid-structured data like images, leveraging spatial dependencies and patterns.
• Existing CNN architectures have limitations influenced by their specific design, input data, and layer configurations.

Purpose of the Study:

• To improve image retrieval rates by fusing established CNN architectures with novel feature extraction techniques.
• To address the gap in understanding how architectural choices and feature engineering impact CNN performance in image retrieval.

Main Methods:

• Integration of GoogLeNet, VGG-19, and ResNet-50 architectures.
• Utilized maximum response-based Eigenvalues for textured features and convolutional Laplacian for scaled object features.
• Employed mapped colored channels for enhanced feature representation.

Main Results:

• Achieved superior image retrieval rates across millions of images from diverse semantic groups and benchmarks.
• Demonstrated high accuracy on challenging datasets like Cifar-10, Corel-10000, and Caltech-256.
• Outperformed state-of-the-art methods on various image complexities, including texture, color, object, and background variations.

Conclusions:

• The proposed fusion model offers a computationally efficient and effective approach for deep learning-based image retrieval.
• This work provides valuable insights into CNN performance and feature engineering for creating innovative image descriptors.
• The method shows significant potential for applications requiring high-accuracy image recognition and retrieval.