Gabor Convolutional Networks (original) (raw)

Learnable Gabor modulated complex-valued networks for orientation robustness

ArXiv, 2020

Robustness to transformation is desirable in many computer vision tasks, given that input data often exhibits pose variance within classes. While translation invariance and equivariance is a documented phenomenon of CNNs, sensitivity to other transformations is typically encouraged through data augmentation. We investigate the modulation of complex valued convolutional weights with learned Gabor filters to enable orientation robustness. With Gabor modulation, the designed network is able to generate orientation dependent features free of interpolation with a single set of rotation-governing parameters. Moreover, by learning rotation parameters alongside traditional convolutional weights, the representation space is not constrained and may adapt to the exact input transformation. We present Learnable Convolutional Gabor Networks (LCGNs), that are parameter-efficient and offer increased model complexity while keeping backpropagation simple. We demonstrate that learned Gabor modulation...

Rotational Invariance Using Gabor Convolution Neural Network and Color Space for Image Processing

International Journal of Ambient Computing and Intelligence

Convolutional neural networks (CNNs) are deep learning methods that are utilized in image processing such as image classification and recognition. It has achieved excellent results in various sectors; however, it still lacks rotation invariant and spatial information. To establish whether two images are rotational versions of one other, one can rotate them exhaustively to see if they compare favorably at some angle. Due to the failure of current algorithms to rotate images and provide spatial information, the study proposes to transform color spaces and use the Gabor filter to address the issue. To gather spatial information, the HSV and CieLab color spaces are used, and Gabor is used to orient images at various orientation. The experiments show that HSV and CieLab color spaces and Gabor convolutional neural network (GCNN) improves image retrieval with an accuracy of 98.72% and 98.67% on the CIFAR-10 dataset.

Robust Gabor Networks

2019

This work takes a step towards investigating the benefits of merging classical vision techniques with deep learning models. Formally, we explore the effect of replacing the first layers of neural network architectures with convolutional layers that are based on Gabor filters with learnable parameters. As a first result, we observe that architectures utilizing Gabor filters as low-level kernels are capable of preserving test set accuracy of deep convolutional networks. Therefore, this architectural change exalts their capabilities in extracting useful low-level features. Furthermore, we observe that the architectures enhanced with Gabor layers gain advantages in terms of robustness when compared to the regular models. Additionally, the existence of a closed mathematical expression for the Gabor kernels allows us to develop an analytical expression for an upper bound to the Lipschitz constant of the Gabor layer. This expression allows us to propose a simple regularizer to enhance the ...

Gabor Filter Initialization And Parameterization Strategies In Convolutional Neural Networks

2019

Convolutional neural networks (CNN) have been widely known in literature to be extremely effective for classifying images. Some of the filters learned during training of the first layer of a CNN resemble the Gabor filter. Gabor filters are extremely good at extracting features within an image. We have taken this as an incentive by replacing the first layer of a CNN with the Gabor filter to increase speed and accuracy for classifying images. We created two simple 5-layer AlexNet-like CNNs comparing grid-search to random-search for initializing the Gabor filter bank. We trained on MNIST, CIFAR-10, and CIFAR-100 as well as a rock dataset created at Western University to study the classification of rock images using a CNN. When training on this rock dataset, we use an architecture from literature and use our Gabor filter substitution method to show the usage of the Gabor filter. Using the Gabor convolutional neural network (GCNN) showed improvements in the training speed across all data...

Deep Learning Techniques to Classify the Aerial Images with Gabor Filter

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Aerial Images are a valuable data source for earth observation, can help us to measure and observe detailed structures on the Earth's surface. Aerial images are drastically growing. This has given particular urgency to the quest for how to make full use of ever-increasing Aerial images for intelligent earth observation. Hence, it is extremely important to understand huge and complex Aerial images. Aerial image classification, which aims at labeling Aerial images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, Aerial image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, recent achievements regarding deep learning for scene classification of Aerial images is still lacking.

A Multi-Scale Convolutional Neural Network for Rotation-Invariant Recognition

Electronics, 2022

The Internet of things (IoT) enables mobile devices to connect and exchange information with others over the Internet with a lot of applications in consumer, commercial, and industrial products. With the rapid development of machine learning, IoT with image recognition capability is a new research area to assist mobile devices with processing image information. In this research, we propose the rotation-invariant multi-scale convolutional neural network (RIMS-CNN) to recognize rotated objects, which are commonly seen in real situations. Based on the dihedral group D4 transformations, the RIMS-CNN equips a CNN with multiple rotated tensors and its processing network. Furthermore, multi-scale features and shared weights are employed in the RIMS-CNN to increase performance. Compared with the data augmentation approach of using rotated images at random angles for training, our proposed method can learn inherent convolution kernels for rotational features. Experiments were conducted on th...