Analysis of food images: Features and classification
MetadataShow full item record
In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work . Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.
Showing items related by title, author, creator and subject.
Chae, J.; Woo, I.; Kim, S.; Maciejewski, R.; Zhu, F.; Delp, E.; Boushey, Carol; Ebert, D. (2011)As obesity concerns mount, dietary assessment methods for prevention and intervention are being developed. These methods include recording, cataloging and analyzing daily dietary records to monitor energy and nutrient ...
He, Y.; Xu, C.; Khanna, N.; Boushey, Carol; Delp, E. (2013)We are developing a dietary assessment system that records daily food intake through the use of food images taken at a meal. The food images are then analyzed to extract the nutrient content in the food. In this paper, ...
Zhu, F.; Bosch, M.; Schap, T.; Khanna, N.; Ebert, D.; Boushey, Carol; Delp, E. (2011)Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain ...