Sequential Learning for Ingredient Recognition From Images

To incorporate the cooking logic into ingredient recognition from food images is beneficial for food cognition. Compared with food categorization, ingredient recognition gives a better understanding on food cognition, by providing crucial information on food compositions. However, there exist situations in which different food are made of different ingredients, thus it is necessary to incorporate cooking logic into ingredient recognition to achieve a better food cognition. Based on this point, our paper proposes a sequential learning method to guide a neural network based (NN-based) model on producing ingredients following the corresponding cooking logic in recipes. Firstly, in order to make a maximum utilization of visual features from images, a double-flow feature fusion module (DFFF) is proposed to obtain features from two image-based, visual tasks (food name proposal and multi-label ingredient proposal). After that, fused features from DFFF, together with original image features, are feed into a bidirectional long short time memory (Bi-LSTM) based ingredient generator to produce sequential ingredients. To guide the sequential ingredient generation process, reinforcement learning is employed by designing a hybrid loss related to both the common and personality traits in ingredients for optimizing the model ability of associating images and sequential ingredients. In addition, sequential ingredients are utilized in a backward flow by reconstructing food images, so that sequential ingredient generation can be further optimized in a complementary manner. In experiments, the results demonstrate the superiority of our method on driving the model to allocate more attention to the correlation between images and sequential ingredients, and produced ingredients are comprehensive and logical.