Results 41 to 50 of about 9,677,595 (330)
Evaluation of Deep Learning on an Abstract Image Classification Dataset
, 2017 Convolutional Neural Networks have become state of the art methods for image classification over the last couple of years. By now they perform better than human subjects on many of the image classification datasets.
Rodriguez-Sanchez, Antonio +1 more
core +1 more source
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology
, 2018 Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level ...
CT Hiley +9 more
core +1 more source
Computer Vision and Pattern Recognition, 2020 In this paper, we address the few-shot classification task from a new perspective of optimal matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to ...
Chi Zhang +3 more
semanticscholar +1 more source
HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification [PDF]
IEEE Geoscience and Remote Sensing Letters, 2019 Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images.
S. K. Roy +3 more
semanticscholar +1 more source
Review of Image Classification Algorithms Based on Convolutional Neural Networks
Remote Sensing, 2021 Image classification has always been a hot research direction in the world, and the emergence of deep learning has promoted the development of this field.
Leiyu Chen +5 more
semanticscholar +1 more source
Fusing image representations for classification using support vector machines
, 2009 In order to improve classification accuracy different image representations are usually combined. This can be done by using two different fusing schemes.
Cherifi, Hocine, Demirkesen, Can
core +3 more sources
Fine-graind Image Classification via Combining Vision and Language
, 2017 Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category.
He, Xiangteng, Peng, Yuxin
core +1 more source
Transfer learning for image classification using VGG19: Caltech-101 image data set
Journal of Ambient Intelligence and Humanized Computing, 2021 Image classification is getting more attention in the area of computer vision. During the past few years, a lot of research has been done on image classification using classical machine learning and deep learning techniques.
Monika Bansal +3 more
semanticscholar +1 more source
A Comparison of AVIRIS and Landsat for Land Use Classification at the Urban Fringe
, 2004 In this study we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado.
Goetz, Alexander F.H. +1 more
core +1 more source
Deep Learning for Hyperspectral Image Classification: An Overview [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2019 Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning ...
Shutao Li +5 more
semanticscholar +1 more source