Clustering meal images in a web-based dietary management system
We investigate the possibility of summarizing meal image collections in an image-based dietary assessment system. A segmentation algorithm detects food regions in each meal image. Pairwise matching of images based on color and texture information of these regions forms a similarity matrix of the image collection. We cluster the nodes of the graph constructed using this matrix, to identify natural groupings of meal images according to their content. Representative images from these clusters form summaries of the large image collection. We conduct a user study to evaluate the effectiveness of the proposed algorithms in summarizing meal image collections, and report the results.