Automated segmentation of optical coherence tomography images

Kharmyssov, Chingis; Ko, Match Wai Lun; Kim, J. R.

doi:10.3788/col201917.011701

Cited by 5 publications

(1 citation statement)

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“…The process of semantic annotation falls into three stages: text-based manual annotation stage, content-based automatic annotation stage, and semantic-based annotation stage. Figure 1 shows the process of semantic annotation [8][9][10][11]. The technique of manually identifying areas in a picture is known as manual annotation.…”

Section: Basic Concepts and Process Of Semantic Annotation Of Imagesmentioning

confidence: 99%

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Wang

Ling

Chen

2022

Preprint

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With the continuous development of mobile and semantic IoT (Internet of Things), the number of news images is increasing sharply. Automatic semantic annotation technology is an important tool to manage and retrieve news images. Therefore, the study is focused on the automatic semantic annotation of news images. First, the problems of the existing automatic annotation methods of news images are analyzed and studied, and then an automatic semantic annotation method of news images based on the fusion of weight and features is proposed. The proposed method extracts the colour features, texture features, and shape features of news images to determine their stability by using the standard deviation of features, and then calculates the weighting coefficient to realize automatic annotation of the news images. The results of the study in the corel5k dataset show that the accuracy of the weight-feature fusion annotation model is 60% for the colour feature, 62% for the texture feature, and 67% for the mixture of colour, shape, and texture. The accuracy of the fusion of weight and feature annotation method is 7% higher than that of the single feature annotation method. The maximum accuracy of the fusion of weight and feature annotation method is 91.2%. The study provides a reference for the research of automatic annotation of news images.

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Section: Basic Concepts and Process Of Semantic Annotation Of Imagesmentioning

confidence: 99%