scholarly journals Unsupervised segmentation of greenhouse plant images based on modified Latent Dirichlet Allocation

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5036 ◽  
Author(s):  
Yi Wang ◽  
Lihong Xu
Keyword(s):  
Image Segmentation ◽  
Latent Dirichlet Allocation ◽  
Mean Shift ◽  
Language Model ◽  
Color Space ◽  
Main Idea ◽  
Coordinate Space ◽  
Unsupervised Segmentation ◽  
Greenhouse Plant ◽  
Dirichlet Allocation

Agricultural greenhouse plant images with complicated scenes are difficult to precisely manually label. The appearance of leaf disease spots and mosses increases the difficulty in plant segmentation. Considering these problems, this paper proposed a statistical image segmentation algorithm MSBS-LDA (Mean-shift Bandwidths Searching Latent Dirichlet Allocation), which can perform unsupervised segmentation of greenhouse plants. The main idea of the algorithm is to take advantage of the language model LDA (Latent Dirichlet Allocation) to deal with image segmentation based on the design of spatial documents. The maximum points of probability density function in image space are mapped as documents and Mean-shift is utilized to fulfill the word-document assignment. The proportion of the first major word in word frequency statistics determines the coordinate space bandwidth, and the spatial LDA segmentation procedure iteratively searches for optimal color space bandwidth in the light of the LUV distances between classes. In view of the fruits in plant segmentation result and the ever-changing illumination condition in greenhouses, an improved leaf segmentation method based on watershed is proposed to further segment the leaves. Experiment results show that the proposed methods can segment greenhouse plants and leaves in an unsupervised way and obtain a high segmentation accuracy together with an effective extraction of the fruit part.

scholarly journals Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera

Agronomy ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 91 ◽  
Author(s):  
Qifan Cao ◽  
Lihong Xu
Keyword(s):  
Latent Dirichlet Allocation ◽  
Language Model ◽  
Surveillance Camera ◽  
Greenhouse Tomato ◽  
Wavelet Features ◽  
Greenhouse Plant ◽  
Time Image ◽  
Training Sets ◽  
Self Adaptive ◽  
Dirichlet Allocation

It has long been a great concern in deep learning that we lack massive data for high-precision training sets, especially in the agriculture field. Plants in images captured in greenhouses, from a distance or up close, not only have various morphological structures but also can have a busy background, leading to huge challenges in labeling and segmentation. This article proposes an unsupervised statistical algorithm SAI-LDA (self-adaptive iterative latent Dirichlet allocation) to segment greenhouse tomato images from a field surveillance camera automatically, borrowing the language model LDA. Hierarchical wavelet features with an overlapping grid word document design and a modified density-based method quick-shift are adopted, respectively, according to different kinds of images, which are classified by specific proportions between fruits, leaves, and the background. We also utilize the feature correlation between several layers of the image to make further optimization through three rounds of iteration of LDA, with updated documents to achieve finer segmentation. Experiment results show that our method can automatically label the organs of the greenhouse plant under complex circumstances, fast and precisely, overcoming the difficulty of inferior real-time image quality caused by a surveillance camera, and thus obtain large amounts of valuable training sets.

Partial Membership Latent Dirichlet Allocation for Soft Image Segmentation

2017 ◽  
Vol 26 (12) ◽  
pp. 5590-5602 ◽  
Author(s):  
Chao Chen ◽  
Alina Zare ◽  
Huy N. Trinh ◽  
Gbenga O. Omotara ◽  
James Tory Cobb ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Latent Dirichlet Allocation ◽  
Dirichlet Allocation

scholarly journals Coral Image Segmentation with Point-Supervision via Latent Dirichlet Allocation with Spatial Coherence

2021 ◽  
Vol 9 (2) ◽  
pp. 157
Author(s):  
Xi Yu ◽  
Bing Ouyang ◽  
Jose C. Principe
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Latent Dirichlet Allocation ◽  
Spatial Coherence ◽  
Image Data ◽  
Feature Space ◽  
Data Sets ◽  
Marine Animals ◽  
Data Set ◽  
Dirichlet Allocation

Deep neural networks provide remarkable performances on supervised learning tasks with extensive collections of labeled data. However, creating such large well-annotated data sets requires a considerable amount of resources, time and effort, especially for underwater images data sets such as corals and marine animals. Therefore, the overreliance on labels is one of the main obstacles for widespread applications of deep learning methods. In order to overcome this need for large annotated dataset, this paper proposes a label-efficient deep learning framework for image segmentation using only very sparse point-supervision. Our approach employs a latent Dirichlet allocation (LDA) with spatial coherence on feature space to iteratively generate pseudo labels. The method requires, as an initial condition, a Wide Residual Network (WRN) trained with sparse labels and mutual information constraints. The proposed method is evaluated on the sparsely labeled coral image data set collected from the Pulley Ridge region in the Gulf of Mexico. Experiments show that our method can improve image segmentation performance against sparsely labeled samples and achieves better results compared with other semi-supervised approaches.

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