Identifying Object States in Cooking-Related Images
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Understanding object states is as important as object recognition for robotic task planning and manipulation. This paper explicitly introduces and addresses the state identification problem in computer vision for the first time. In this paper, objects and ingredients in cooking videos are explored and the most frequent objects are analyzed. Eleven states from the most frequent cooking objects are examined and a dataset of images containing those objects and their states is created. As a solution to the state identification problem, a Resnet based deep model is proposed. The model is initialized with Imagenet weights and trained on the dataset of eleven classes. The trained state identification model is evaluated on a subset of the Imagenet dataset and state labels are provided using a combination of the model with manual checking. Moreover, an individual model is fine-tuned for each object in the dataset using the initially trained model and object-specific images, where significant improvement is demonstrated.
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