Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation

The task of image segmentation is to classify each pixel in the image based on the appropriate label. Various deep learning approaches have been proposed for image segmentation that offers high accuracy and deep architecture. However, the deep learning technique uses a pixel-wise loss function for the training process. Using pixel-wise loss neglected the pixel neighbor relationships in the network learning process. The neighboring relationship of the pixels is essential information in the image. Utilizing neighboring pixel information provides an advantage over using only pixel-to-pixel information. This study presents regularizers to give the pixel neighbor relationship information to the learning process. The regularizers are constructed by the graph theory approach and topology approach: By graph theory approach, graph Laplacian is used to utilize the smoothness of segmented images based on output images and ground-truth images. By topology approach, Euler characteristic is used to identify and minimize the number of isolated objects on segmented images. Experiments show that our scheme successfully captures pixel neighbor relations and improves the performance of the convolutional neural network better than the baseline without a regularization term

Adaptive Neuron-wise Discriminant Criterion and Adaptive Center Loss at Hidden Layer for Deep Convolutional Neural Network

A deep convolutional neural network (CNN) has been widely used in image classification and gives better classification accuracy than the other techniques. The softmax cross-entropy loss function is often used for classification tasks. There are some works to introduce the additional terms in the objective function for training to make the features of the output layer more discriminative. The neuron-wise discriminant criterion makes the input feature of each neuron in the output layer discriminative by introducing the discriminant criterion to each of the features. Similarly, the center loss was introduced to the features before the softmax activation function for face recognition to make the deep features discriminative. The ReLU function is often used for the network as an active function in the hidden layers of the CNN. However, it is observed that the deep features trained by using the ReLU function are not discriminative enough and show elongated shapes. In this paper, we propose to use the neuron-wise discriminant criterion at the output layer and the center-loss at the hidden layer. Also, we introduce the online computation of the means of each class with the exponential forgetting. We named them adaptive neuron-wise discriminant criterion and adaptive center loss, respectively. The effectiveness of the integration of the adaptive neuron-wise discriminant criterion and the adaptive center loss is shown by the experiments with MNSIT, FashionMNIST, CIFAR10, CIFAR100, and STL10. Source code is at https://github.com/i13abe/Adaptive-discriminant-and-centerComment: Accepted to IJCNN 202

Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images

This paper presents a novel image representation method for generic object recognition by using higher-order local autocorrelations on posterior probability images. The proposed method is an extension of the bag-of-features approach to posterior probability images. The standard bag-of-features approach is approximately thought of as a method that classifies an image to a category whose sum of posterior probabilities on a posterior probability image is maximum. However, by using local autocorrelations of posterior probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results reveal that the proposed method exhibits higher classification performances than the standard bag-of-features method