Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm.
Kai TianJiuhao LiJiefeng ZengAsenso EvansLina ZhangPublished in: Comput. Electron. Agric. (2019)
Keyphrases
- k means
- segmentation algorithm
- cluster centers
- clustering method
- segmentation method
- test images
- clustering algorithm
- adaptive thresholding
- computational complexity
- image analysis
- data clustering
- gray level images
- hierarchical clustering
- rough k means
- clustering quality
- matching algorithm
- image segmentation algorithms
- unsupervised clustering
- cluster analysis
- thresholding algorithm
- fuzzy k means
- image segmentation algorithm
- gradient information
- region of interest
- spectral clustering
- image matching
- expectation maximization
- image segmentations
- self organizing maps
- input image
- image segments
- unsupervised segmentation
- ground truth
- data points
- thresholding method
- center based clustering
- fuzzy clustering algorithm
- initial cluster centers
- clustering approaches
- semi supervised clustering
- normalized cut
- segmented images
- region growing
- level set
- image retrieval
- similarity measure
- image segmentation