In the recent advancement of multimedia technologies, it becomes a major concern of detecting visual attention regions in the field of image processing. The popularity of the terminal devices in a heterogeneous environment of the multimedia technology gives us enough scope for the betterment of image visualization. Although there exist numerous methods, feature based image extraction becomes a popular one in the field of image processing. The objective of image segmentation is the domain-independent partition of the image into a set of regions, which are visually distinct and uniform with respect to some property, such as grey level, texture or colour. Segmentation and subsequent extraction can be considered the first step and key issue in object recognition, scene understanding and image analysis. Its application area encompasses mobile devices, industrial quality control, medical appliances, robot navigation, geophysical exploration, military applications, etc. In all these areas, the quality of the final results depends largely on the quality of the preprocessing work. Most of the times, acquiring spurious-free preprocessing data requires a lot of application cum mathematical intensive background works. We propose a feature based fuzzy rule guided novel technique that is functionally devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE) and Peak Signal to Noise Ratio (PSNR).
Indian Institute of Science Education and Research, Pune（印度科学教育与研究学院浦那分校）；Visva-Bharati University（维斯瓦巴拉蒂大学）；RCC Institute of Information Technology（RCC信息技术学院）
FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification
School of Information Science and Technology, Nantong University（南通大学信息科学与技术学院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；School of Computer Science and Technology, Nantong University（南通大学计算机科学与技术学院）；Centre for Artificial Intelligence, FEIT, University of Technology Sydney（悉尼科技大学工程与信息技术学院人工智能中心）
Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pooling, which implements dimensionality reduction. The impact of pooling in the classification performance of the CNNs has been highlighted in several previous works, and a variety of alternative pooling operators have been proposed. However, only a few of them tackle with the uncertainty that is naturally propagated from the input layer to the feature maps of the hidden layers through convolutions. In this paper we pre…
Jiangnan University（江南大学）；East China Institute of Technology（东华理工大学）
The methods of extracting image features are the key to many image processing tasks. At present, the most popular method is the deep neural network which can automatically extract robust features through end-to-end training instead of hand-crafted feature extraction. However, the deep neural network currently faces many challenges: 1) its effectiveness is heavily dependent on large datasets, so the computational complexity is very high; 2) it is usually regarded as a black box model with poor interpretability. To meet the above challenges, a more interpretable and scalable feature learning met…
FTCFormer: Fuzzy Token Clustering Transformer for Image Classification
School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China（西安交通大学利物浦大学先进技术学院）；Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom（利物浦大学数学科学系）；Department of Computer Science, University of Liverpool, Liverpool, United Kingdom（利物浦大学计算机科学系）
