21st International Conference on Pattern Recognition (ICPR 2012) November 11-15, 2012. Tsukuba, Japan Multiple-Food Recognition Considering Co-occurrence Employing Manifold Ranking Yuji Matsuda and Keiji Yanai Graduate School of Informatics, The University of Electro-Communications Email:
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[email protected] Abstract In this paper, we propose a method to recog- nize food images which include multiple food items Figure 1. Examples of multiple-food pho- considering co-occurrence statistics of food items. tos. Theproposedmethodemploysamanifoldrank- ing method which has been applied to image re- search of scene recognition the targets of which usu- trieval successfully in the literature. In the exper- ally contain multiple objects, relations between ob- iments, we prepared co-occurrence matrices of 100 jects were considered as important cue for scene food items using various kinds of data sources in- recognition in some works [8, 9]. Inspired by these cluding Web texts, Web food blogs and our own food works, we introduce relation information between database, and evaluated the final results obtained by food items for recognizing multiple-food meal pho- applying manifold ranking. As results, it has been tos. As relations which have been used in object proved that co-occurrence statistics obtained from a recognition research so far, co-occurrence [8] and food photo database is very helpful to improve the relative location [9] are common. In case of meal classification rate within the top ten candidates. photos, we think co-occurrence relation is more im- portant than relative locations, because some com- 1 Introduction binations of foods such as “hamburger and french Recently, personal services to recode people’s fries” are very common, while the way to place food food habits by taking meal photos with mobile items on the table is not strictly restricted in gen- phones have become popular.