Cooking State Recognition from Images Using Inception Architecture

Salekin, Sirajus; Jelodar, Ahmad Babaeian; Kushol, Rafsanjany

doi:10.1109/icrest.2019.8644262

Cited by 16 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it was shown in [17], freezing more layers in our model started to decrease our accuracy, and also increased our training time significantly. The Inception V3 model was trained with the purpose of object detection.…”

Section: ) Fine-tuning Layersmentioning

confidence: 53%

Tuned Inception V3 for Recognizing States of Cooking Ingredients

Ng¹

2019

State Recognition Symposium

View full text Add to dashboard Cite

Cooking is a task that must be performed in a daily basis, and thus it is an activity that many people take for granted. For humans preparing a meal comes naturally, but for robots even preparing a simple sandwich results in an extremely difficult task. In robotics, designing kitchen robots is complicated since cooking relies on a variety of physical interactions that are dependent on different conditions such as changes in the environment, proper execution of sequential instructions, along with motions, and detection of the different states in which cooking-ingredients can be in for their correct grasping and manipulation. In this paper, we focus on the challenge of state recognition and propose a fine tuned convolutional neural network that makes use of transfer learning by reusing the Inception V3 pre-trained model. The model is trained and validated on a cooking dataset consisting of eleven states (e.g. peeled, diced, whole, etc.). The work presented on this paper could provide insight into finding a potential solution to the problem.

show abstract

Section: ) Fine-tuning Layersmentioning

confidence: 53%

Tuned Inception V3 for Recognizing States of Cooking Ingredients

Ng¹

2019

State Recognition Symposium

View full text Add to dashboard Cite

show abstract

“…This strategy showed significant improvement with respect to a food-independent model. The Inception V3 architecture is used instead in [16] CNNs are exploited also for other food-related tasks such as food localization, segmentation, ingredients recognition, quantity, and calories estimation. Readers interested in these tasks can refer to [60] and [61] for a comprehensive survey of recent techniques.…”

Section: B Learned Featuresmentioning

confidence: 99%

“…Food state recognition is a topic that has not been extensively studied. The only previous works that tackled this problem are those by Jelodar et al [15] that first introduced a new food state challenge dataset, and Salekin et al [16].…”

Section: Introductionmentioning

confidence: 99%

State Recognition of Food Images Using Deep Features

2020

View full text Add to dashboard Cite

State recognition of food images is a recent topic that is gaining a huge interest in the Computer Vision community. Recently, researchers presented a dataset of food images at different states where unfortunately no information regarding the food category was included. In practical food monitoring applications it is important to be able to recognize a peeled tomato instead of a generic peeled item. To this end, in this paper, we introduce a new dataset containing 20 different food categories taken from fruits and vegetables at 11 different states ranging from solid, sliced to creamy paste. We experiment with most common Convolutional Neural Network (CNN) architectures on three different recognition tasks: food categories, food states, and both food categories and states. Since lack of labeled data is a common situation in practical applications, here we exploits deep features extracted from CNNs combined with Support Vector Machines (SVMs) as an alternative to the End-to-End classification. We also compare deep features with several hand-crafted features. These experiments confirm that deep features outperform hand-crafted features on all the three classification tasks and whatever is the food category or food state considered. Finally, we test the generalization capability of the most performing deep features by using another, publicly available, dataset of food states. This last experiment shows that the features extracted from a CNN trained on our proposed dataset achieve performance quite close to the one achieved by the state of the art method. This confirms that our deep features are robust with respect to data never seen by the CNN.

show abstract

“…Finally, for recognition task, they used a ResNet based fine-tuned classification model which obtained decent accuracy in benchmark dataset. There are some other studies [7]- [9] available in the literature that also use transfer learning approach [2] for cooking state recognition, in which researchers modeled the cooking state classification as a seven class classification problem to recognize the state of a given cooking object using fine-tuned VGG16, Inception V3 architectures [10] with some additional layers.…”

Section: Related Workmentioning

confidence: 99%

Cooking State Recognition from Images via Fine-Tuned VGG19 Model

Uddin¹

2019

State Recognition Symposium

View full text Add to dashboard Cite

In order to perform automated tasks such as cooking, an agent (robot) must have a good understanding of cooking objects, and interaction between numerous cooking objects to carry out a set of actions such as grasping, manipulation. In this work, we focus on training computer (robot) how to recognize different cooking objects with a specific cooking state from cooking instructional videos using convolutional neural networks (fine-tuned VGG19 based model). Our experimental results show that our model recognizes cooking states with an accuracy of 71.4% and 68.4% on validation and test dataset, respectively.

show abstract

Cooking State Recognition from Images Using Inception Architecture

Cited by 16 publications

References 16 publications

Tuned Inception V3 for Recognizing States of Cooking Ingredients

Tuned Inception V3 for Recognizing States of Cooking Ingredients

State Recognition of Food Images Using Deep Features

Cooking State Recognition from Images via Fine-Tuned VGG19 Model

Contact Info

Product

Resources

About