Abstract
The process of identifying food items from an image is one of the promising applications of visual object recognition in computer vision. However, analysis of food items is a particularly challenging task due to the nature of their has achieved by traditional approaches in the field. Deep neural networks have exceeded such solutions. With a goal to successfully applying computer images, which is why a low classification accuracy vision techniques to classify food images based on Inception-v3 model of TensorFlow platform, we use the transfer learning technology to retrain the food category datasets. Our approach shows auspicious results with an average accuracy of 95.2% approximately in correctly recognizing among 7 traditional Bangladeshi foods.
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References
Inception-v3. https://cloud.google.com/tpu/docs/inception-v3-advanced. Accessed 1 Sept 2018
TensorFlow. https://www.packtpub.com/mapt/book/big_data_and_business_intelligence/9781786468574/1/ch01lvl1sec9/tensorflow–a-general-overview. Accessed 2 Sept 2018
Transfer learning. https://searchcio.techtarget.com/definition/transfer-learning. Accessed 3 Sept 2018
Islam, M.S., Foysal, F.A., Neehal, N.: InceptB: a CNN Based classification approach for recognizing traditional Bengali games. Procedia Comput. Sci. 143, 592–602 (2018)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Convolutional neural network architecture. http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/. Accessed 5 Sept 2018
The Architecture of Convolutional Neural Network. https://www.mdpi.com/10994300/19/6/242/htm. Accessed 12 Sept 2018
Convolutional Neural Network. https://wiki.tum.de/display/lfdv/Layers+of+a+Convolutional+Neural+Network. Accessed 5 Sept 2018
Flower category datasets. https://datascience.stackexchange.com/questions/15989/microaverage-vs-macro-average-performance-in-a-multiclass-classification. Accessed 6 Sept 2018
Xia, X., Xu, C., Nan, B.: Inception-v3 for flower classification. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 783–787. IEEE (2017)
Mathew, A., Mathew, J., Govind, M., Mooppan, A.: An improved transfer learning approach for intrusion detection. In: 7th International Conference on Advances in Computing and Communications, Cochin, India (2017)
Kieffer, B., Babaie, M., Kalra, S., Tizhoosh, H.R.: Convolutional neural networks for histopathology image classification: training vs. using pre-trained networks. In: 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6. IEEE (2017)
Xia, X.L., Xu, C., Nan, B.: Facial expression recognition based on tensorflow platform. In: ITM Web of Conferences, vol. 12, p. 01005. EDP Sciences (2017)
Batsukh, B.E., Tsend, G.: Effective computer model for recognizing nationality from frontal image (2016)
Support vector machine. https://en.wikipedia.org/wiki/Support_vector_machine. Accessed 13 Sept 2018
Active appearance model. https://en.wikipedia.org/wiki/Active_appearance_model. Accessed 15 Sept 2018
Cootes, T., Baldock, E.R., Graham, J.: An introduction to active shape models, pp. 223–248 (2000)
Backpropagation algorithm. https://en.wikipedia.org/wiki/Backpropagation. Accessed 11 Sept 2018
Cross-entropy. https://en.wikipedia.org/wiki/Cross_entropy. Accessed 8 Sept 2018
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Tasnim, N., Romyull Islam, M., Shuvo, S.B. (2020). A Convolution Neural Network Based Classification Approach for Recognizing Traditional Foods of Bangladesh from Food Images. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_79
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DOI: https://doi.org/10.1007/978-3-030-16657-1_79
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