4 research outputs found
AFS: An Attention-based mechanism for Supervised Feature Selection
As an effective data preprocessing step, feature selection has shown its
effectiveness to prepare high-dimensional data for many machine learning tasks.
The proliferation of high di-mension and huge volume big data, however, has
brought major challenges, e.g. computation complexity and stability on noisy
data, upon existing feature-selection techniques. This paper introduces a novel
neural network-based feature selection architecture, dubbed Attention-based
Feature Selec-tion (AFS). AFS consists of two detachable modules: an at-tention
module for feature weight generation and a learning module for the problem
modeling. The attention module for-mulates correlation problem among features
and supervision target into a binary classification problem, supported by a
shallow attention net for each feature. Feature weights are generated based on
the distribution of respective feature se-lection patterns adjusted by
backpropagation during the train-ing process. The detachable structure allows
existing off-the-shelf models to be directly reused, which allows for much less
training time, demands for the training data and requirements for expertise. A
hybrid initialization method is also intro-duced to boost the selection
accuracy for datasets without enough samples for feature weight generation.
Experimental results show that AFS achieves the best accuracy and stability in
comparison to several state-of-art feature selection algo-rithms upon both
MNIST, noisy MNIST and several datasets with small samples.Comment: 9 pages, 5 figures, published in the AAAI 201
Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning
Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat image. It is difficult for traditional image processing methods to solve occlusion problem due to the deficiency of high-level semantic features, while existing deep learning based counting methods did not solve the occlusion efficiently. This article proposes an improved EfficientDet-D0 object detection model for wheat ear counting, and focuses on solving occlusion. First, the transfer learning method is employed in the pre-training of the model backbone network to extract the high-level semantic features of wheat ears. Secondly, an image augmentation method Random-Cutout is proposed, in which some rectangles are selected and erased according to the number and size of the wheat ears in the images to simulate occlusion in real wheat images. Finally, convolutional block attention module (CBAM) is adopted into the EfficientDet-D0 model after the backbone, which makes the model refine the features, pay more attention to the wheat ears and suppress other useless background information. Extensive experiments are done by feeding the features to detection layer, showing that the counting accuracy of the improved EfficientDet-D0 model reaches 94%, which is about 2% higher than the original model, and false detection rate is 5.8%, which is the lowest among comparative methods