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

ABSTRACT: 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 maj

SUBMITTER: Wang Y 

PROVIDER: S-EPMC6026534 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature