Name
Abstract | ||
|---|---|---|
In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%. |
Year | Venue | Keywords |
|---|---|---|
2013 | 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE) | image recognition,support vector machines,image segmentation |
Field | DocType | ISSN |
Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Image texture,Segmentation,Support vector machine,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing,Minimum spanning tree-based segmentation | Conference | 2471-7819 |
Citations | PageRank | References |
14 | 0.78 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marios Anthimopoulos | 1 | 247 | 13.75 |
| Joachim Dehais | 2 | 41 | 3.92 |
| Peter Diem | 3 | 65 | 5.19 |
| Stavroula G Mougiakakou | 4 | 342 | 28.61 |