2 research outputs found
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification
Traditional Active/Self/Interactive Learning for Hyperspectral Image
Classification (HSIC) increases the size of the training set without
considering the class scatters and randomness among the existing and new
samples. Second, very limited research has been carried out on joint
spectral-spatial information and finally, a minor but still worth mentioning is
the stopping criteria which not being much considered by the community.
Therefore, this work proposes a novel fuzziness-based spatial-spectral within
and between for both local and global class discriminant information preserving
(FLG) method. We first investigate a spatial prior fuzziness-based
misclassified sample information. We then compute the total local and global
for both within and between class information and formulate it in a
fine-grained manner. Later this information is fed to a discriminative
objective function to query the heterogeneous samples which eliminate the
randomness among the training samples. Experimental results on benchmark HSI
datasets demonstrate the effectiveness of the FLG method on Generative, Extreme
Learning Machine and Sparse Multinomial Logistic Regression (SMLR)-LORSAL
classifiers.Comment: 13 pages, 7 figure
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
Deep learning has demonstrated tremendous success in variety of application
domains in the past few years. This new field of machine learning has been
growing rapidly and applied in most of the application domains with some new
modalities of applications, which helps to open new opportunity. There are
different methods have been proposed on different category of learning
approaches, which includes supervised, semi-supervised and un-supervised
learning. The experimental results show state-of-the-art performance of deep
learning over traditional machine learning approaches in the field of Image
Processing, Computer Vision, Speech Recognition, Machine Translation, Art,
Medical imaging, Medical information processing, Robotics and control,
Bio-informatics, Natural Language Processing (NLP), Cyber security, and many
more. This report presents a brief survey on development of DL approaches,
including Deep Neural Network (DNN), Convolutional Neural Network (CNN),
Recurrent Neural Network (RNN) including Long Short Term Memory (LSTM) and
Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN),
Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). In
addition, we have included recent development of proposed advanced variant DL
techniques based on the mentioned DL approaches. Furthermore, DL approaches
have explored and evaluated in different application domains are also included
in this survey. We have also comprised recently developed frameworks, SDKs, and
benchmark datasets that are used for implementing and evaluating deep learning
approaches. There are some surveys have published on Deep Learning in Neural
Networks [1, 38] and a survey on RL [234]. However, those papers have not
discussed the individual advanced techniques for training large scale deep
learning models and the recently developed method of generative models [1].Comment: 39 pages, 46 figures, 3 tables. arXiv admin note: text overlap with
arXiv:1408.3264, arXiv:1411.404