Learning CNN filters from user-drawn image markers for coconut-tree image classification

• URL: http://arxiv.org/abs/2008.03549v2
• Date: Thu, 27 Aug 2020 23:02:43 GMT
• Title: Learning CNN filters from user-drawn image markers for coconut-tree image classification
• Authors: Italos Estilon de Souza and Alexandre Xavier Falc\~ao
• Abstract summary: We present a method that needs a minimal set of user-selected images to train the CNN's feature extractor. The method learns the filters of each convolutional layer from user-drawn markers in image regions that discriminate classes. It does not rely on optimization based on backpropagation, and we demonstrate its advantages on the binary classification of coconut-tree aerial images.
• Score: 78.42152902652215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying species of trees in aerial images is essential for land-use classification, plantation monitoring, and impact assessment of natural disasters. The manual identification of trees in aerial images is tedious, costly, and error-prone, so automatic classification methods are necessary. Convolutional Neural Network (CNN) models have well succeeded in image classification applications from different domains. However, CNN models usually require intensive manual annotation to create large training sets. One may conceptually divide a CNN into convolutional layers for feature extraction and fully connected layers for feature space reduction and classification. We present a method that needs a minimal set of user-selected images to train the CNN's feature extractor, reducing the number of required images to train the fully connected layers. The method learns the filters of each convolutional layer from user-drawn markers in image regions that discriminate classes, allowing better user control and understanding of the training process. It does not rely on optimization based on backpropagation, and we demonstrate its advantages on the binary classification of coconut-tree aerial images against one of the most popular CNN models.

Related papers

• Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification [17.373054348176932]
  In this work, a post-hoc interpretation tool named feature activation map (FAM) is proposed. FAM can interpret deep learning models without FC layers as a classifier. Experiments conducted on ten deep learning models for few-shot image classification, contrastive learning image classification and image retrieval tasks demonstrate the effectiveness of the proposed FAM algorithm.
  arXiv  Detail & Related papers  (2023-07-11T05:33:46Z)
• Facilitated machine learning for image-based fruit quality assessment in developing countries [68.8204255655161]
  Automated image classification is a common task for supervised machine learning in food science. We propose an alternative method based on pre-trained vision transformers (ViTs) It can be easily implemented with limited resources on a standard device.
  arXiv  Detail & Related papers  (2022-07-10T19:52:20Z)
• Weakly-supervised fire segmentation by visualizing intermediate CNN layers [82.75113406937194]
  Fire localization in images and videos is an important step for an autonomous system to combat fire incidents. We consider weakly supervised segmentation of fire in images, in which only image labels are used to train the network. We show that in the case of fire segmentation, which is a binary segmentation problem, the mean value of features in a mid-layer of classification CNN can perform better than conventional Class Activation Mapping (CAM) method.
  arXiv  Detail & Related papers  (2021-11-16T11:56:28Z)
• ResMLP: Feedforward networks for image classification with data-efficient training [73.26364887378597]
  We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. We will share our code based on the Timm library and pre-trained models.
  arXiv  Detail & Related papers  (2021-05-07T17:31:44Z)
• The Mind's Eye: Visualizing Class-Agnostic Features of CNNs [92.39082696657874]
  We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer. Our method uses a dual-objective activation and distance loss, without requiring a generator network nor modifications to the original model.
  arXiv  Detail & Related papers  (2021-01-29T07:46:39Z)
• Learning degraded image classification with restoration data fidelity [0.0]
  We investigate the influence of degradation types and levels on four widely-used classification networks. We propose a novel method leveraging a fidelity map to calibrate the image features obtained by pre-trained networks. Our results reveal that the proposed method is a promising solution to mitigate the effect caused by image degradation.
  arXiv  Detail & Related papers  (2021-01-23T23:47:03Z)
• Convolutional Neural Networks from Image Markers [62.997667081978825]
  Feature Learning from Image Markers (FLIM) was recently proposed to estimate convolutional filters, with no backpropagation, from strokes drawn by a user on very few images. This paper extends FLIM for fully connected layers and demonstrates it on different image classification problems. The results show that FLIM-based convolutional neural networks can outperform the same architecture trained from scratch by backpropagation.
  arXiv  Detail & Related papers  (2020-12-15T22:58:23Z)
• Convolutional Neural Networks for Multispectral Image Cloud Masking [7.812073412066698]
  Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks. We study the use of different CNN architectures for cloud masking of Proba-V multispectral images.
  arXiv  Detail & Related papers  (2020-12-09T21:33:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.