Improving invisible food texture detection by using adaptive extremal region detector in food recognition

• Main Authors: Razali, Mohd Norhisham, Manshor, Noridayu, Mustapha, Norwati, Yaakob, Razali, Zainudin, Muhammad Noorazlan Shah
• Format: Article
• Language: English
• Published: World Academy of Research in Science and Engineering 2019
• Online Access: http://psasir.upm.edu.my/id/eprint/80199/1/Improving%20invisible%20food%20texture%20detection%20by%20using%20adaptive%20extremal%20region%20detector%20in%20food%20recognition.pdf; http://psasir.upm.edu.my/id/eprint/80199/; https://www.researchgate.net/publication/336155020_Improving_Invisible_Food_Texture_Detection_by_using_Adaptive_Extremal_Region_Detector_in_Food_Recognition

The advancement of mobile technology with reasonable cost has indulge the mobile phone users to photograph foods and shared in social media. Since that, food recognition has become emerging research area in image processing and machine learning. Food recognition provides an automatic identification of the types of foods from an image. Then, further analysis in food recognition is performed to approximate the calories and nutritional information that can be used for health-care purposes. The interest region-based detector by using Maximally Stable Extremal Region (MSER) may provides distinctive interest points by representing the arbitrary shape of foods through global segmentation especially the food images with strong mixture of ingredients. However, the classification performance on food categories with less diverse texture food images by using MSER are obviously low compared to the other food categories that have more noticeable texture. The texture-less food objects were suffered from small number of extremal regions (ER) detection beside having low image brightness and small resolutions. Therefore, this paper proposed an adaptive interest regions detection by using MSER (aMSER) that provide a mechanism to choose appropriate MSER parameter configuration to increase the density of interest points on the targeted food images. The features are described by using Speeded-up Robust Feature Transform (SURF) and encoded by using Bag of Features (BoF) model. The classification is performed by using Linear Support Vector Machine and yield 84.20% classification rate on UEC100-Food dataset with competitive number of ER and computation cost.