Skip to main content

2019 | OriginalPaper | Buchkapitel

Evaluating CNN-Based Semantic Food Segmentation Across Illuminants

verfasst von : Gianluigi Ciocca, Davide Mazzini, Raimondo Schettini

Erschienen in: Computational Color Imaging

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this paper we aim to explore the potential of Deep Convolutional Neural Networks (DCNNs) on food image segmentation where semantic segmentation paradigm is used to separate food regions from the non-food regions. Specifically, we are interested in evaluating the performance of an efficient DCNN with respect to variability in illumination conditions that can be found in food images taken in real scenarios. To this end we have designed an experimental setup where the network is trained on images rendered as if they were taken under nine different illuminants. We evaluate the food vs. non-food segmentation performance of the network in terms of standard Intersection over Union (IoU) measure. The results of this experimentation are reported and discussed.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S., et al.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRef Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S., et al.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRef
2.
Zurück zum Zitat Anthimopoulos, M.M., Gianola, L., Scarnato, L., Diem, P., Mougiakakou, S.G.: A food recognition system for diabetic patients based on an optimized bag-of-features model. Biomed. Health Inform. 18(4), 1261–1271 (2014)CrossRef Anthimopoulos, M.M., Gianola, L., Scarnato, L., Diem, P., Mougiakakou, S.G.: A food recognition system for diabetic patients based on an optimized bag-of-features model. Biomed. Health Inform. 18(4), 1261–1271 (2014)CrossRef
4.
Zurück zum Zitat Aslan, S., Ciocca, G., Schettini, R.: Semantic food segmentation for automatic dietary monitoring. In: IEEE 8th International Conference on Consumer Electronics, Berlin (ICCE-Berlin), pp. 1–6 (2018) Aslan, S., Ciocca, G., Schettini, R.: Semantic food segmentation for automatic dietary monitoring. In: IEEE 8th International Conference on Consumer Electronics, Berlin (ICCE-Berlin), pp. 1–6 (2018)
5.
Zurück zum Zitat Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRef Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRef
6.
Zurück zum Zitat Bianco, S., Cadene, R., Celona, L., Napoletano, P.: Benchmark analysis of representative deep neural network architectures. IEEE Access 6, 64270–64277 (2018)CrossRef Bianco, S., Cadene, R., Celona, L., Napoletano, P.: Benchmark analysis of representative deep neural network architectures. IEEE Access 6, 64270–64277 (2018)CrossRef
8.
Zurück zum Zitat Bianco, S., Cusano, C., Piccoli, F., Schettini, R.: Artistic photo filter removal using convolutional neural networks. J. Electron. Imaging 27(1), 011004 (2017)CrossRef Bianco, S., Cusano, C., Piccoli, F., Schettini, R.: Artistic photo filter removal using convolutional neural networks. J. Electron. Imaging 27(1), 011004 (2017)CrossRef
9.
Zurück zum Zitat Bolanos, M., Radeva, P.: Simultaneous food localization and recognition. In: 23rd IEEE International Conference on Pattern Recognition (ICPR), pp. 3140–3145 (2016) Bolanos, M., Radeva, P.: Simultaneous food localization and recognition. In: 23rd IEEE International Conference on Pattern Recognition (ICPR), pp. 3140–3145 (2016)
10.
Zurück zum Zitat Bosch, M., Zhu, F., Khanna, N., Boushey, C., Delp, E.: Combining global and local features for food identification in dietary assessment. In: 18th IEEE International Conference on Image Processing (ICIP), pp. 1789–1792 (2011) Bosch, M., Zhu, F., Khanna, N., Boushey, C., Delp, E.: Combining global and local features for food identification in dietary assessment. In: 18th IEEE International Conference on Image Processing (ICIP), pp. 1789–1792 (2011)
11.
Zurück zum Zitat Buzzelli, M., van de Weijer, J., Schettini, R.: Learning illuminant estimation from object recognition. In: 25th IEEE International Conference on Image Processing (ICIP), pp. 3234–3238 (2018) Buzzelli, M., van de Weijer, J., Schettini, R.: Learning illuminant estimation from object recognition. In: 25th IEEE International Conference on Image Processing (ICIP), pp. 3234–3238 (2018)
12.
Zurück zum Zitat Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected crfs. In: International Conference on Learning Representations (2015) Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected crfs. In: International Conference on Learning Representations (2015)
13.
Zurück zum Zitat Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)CrossRef Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)CrossRef
14.
Zurück zum Zitat Ciocca, G., Marini, D., Rizzi, A., Schettini, R., Zuffi, S.: Retinex preprocessing of uncalibrated images for color-based image retrieval. J. Electron. Imaging 12(1), 161–172 (2003)CrossRef Ciocca, G., Marini, D., Rizzi, A., Schettini, R., Zuffi, S.: Retinex preprocessing of uncalibrated images for color-based image retrieval. J. Electron. Imaging 12(1), 161–172 (2003)CrossRef
16.
Zurück zum Zitat Ciocca, G., Napoletano, P., Schettini, R.: Food recognition: a new dataset, experiments and results. IEEE J. Biomed. Health Inform. 21(3), 588–598 (2017)CrossRef Ciocca, G., Napoletano, P., Schettini, R.: Food recognition: a new dataset, experiments and results. IEEE J. Biomed. Health Inform. 21(3), 588–598 (2017)CrossRef
17.
Zurück zum Zitat Dehais, J., Anthimopoulos, M., Mougiakakou, S.: Food image segmentation for dietary assessment. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, pp. 23–28. ACM (2016) Dehais, J., Anthimopoulos, M., Mougiakakou, S.: Food image segmentation for dietary assessment. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, pp. 23–28. ACM (2016)
18.
Zurück zum Zitat Deng, Y., Manjunath, B.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800–810 (2001)CrossRef Deng, Y., Manjunath, B.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800–810 (2001)CrossRef
19.
Zurück zum Zitat Ege, T., Yanai, K.: Simultaneous estimation of food categories and calories with multi-task CNN. In: Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 198–201. IEEE (2017) Ege, T., Yanai, K.: Simultaneous estimation of food categories and calories with multi-task CNN. In: Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 198–201. IEEE (2017)
20.
Zurück zum Zitat Ege, T., Yanai, K.: Multi-task learning of dish detection and calorie estimation. In: Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management, pp. 53–58. ACM (2018) Ege, T., Yanai, K.: Multi-task learning of dish detection and calorie estimation. In: Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management, pp. 53–58. ACM (2018)
21.
Zurück zum Zitat He, Y., Khanna, N., Boushey, C.J., Delp, E.J.: Image segmentation for image-based dietary assessment: a comparative study. In: IEEE International Symposium on Signals, Circuits and Systems (ISSCS), pp. 1–4 (2013) He, Y., Khanna, N., Boushey, C.J., Delp, E.J.: Image segmentation for image-based dietary assessment: a comparative study. In: IEEE International Symposium on Signals, Circuits and Systems (ISSCS), pp. 1–4 (2013)
23.
Zurück zum Zitat Joutou, T., Yanai, K.: A food image recognition system with multiple kernel learning. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 285–288. IEEE (2009) Joutou, T., Yanai, K.: A food image recognition system with multiple kernel learning. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 285–288. IEEE (2009)
24.
Zurück zum Zitat Kawano, Y., Yanai, K.: Foodcam: a real-time food recognition system on a smartphone. Multimed. Tools Appl. 74(14), 5263–5287 (2015)CrossRef Kawano, Y., Yanai, K.: Foodcam: a real-time food recognition system on a smartphone. Multimed. Tools Appl. 74(14), 5263–5287 (2015)CrossRef
25.
Zurück zum Zitat Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015) Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
26.
Zurück zum Zitat Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3431–3440 (2015) Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3431–3440 (2015)
27.
Zurück zum Zitat Mazzini, D., Buzzelli, M., Pauy, D.P., Schettini, R.: A CNN architecture for efficient semantic segmentation of street scenes. In: IEEE 8th International Conference on Consumer Electronics, Berlin (ICCE-Berlin), pp. 1–6 (2018) Mazzini, D., Buzzelli, M., Pauy, D.P., Schettini, R.: A CNN architecture for efficient semantic segmentation of street scenes. In: IEEE 8th International Conference on Consumer Electronics, Berlin (ICCE-Berlin), pp. 1–6 (2018)
28.
Zurück zum Zitat Mazzini, D.: Guided upsampling network for real-time semantic segmentation. In: British Machine Vision Conference, BMVC 2018, Northumbria University, Newcastle, 3–6 September 2018, p. 117 (2018) Mazzini, D.: Guided upsampling network for real-time semantic segmentation. In: British Machine Vision Conference, BMVC 2018, Northumbria University, Newcastle, 3–6 September 2018, p. 117 (2018)
29.
Zurück zum Zitat Mezgec, S., Koroušić Seljak, B.: Nutrinet: a deep learning food and drink image recognition system for dietary assessment. Nutrients 9(7), 657 (2017)CrossRef Mezgec, S., Koroušić Seljak, B.: Nutrinet: a deep learning food and drink image recognition system for dietary assessment. Nutrients 9(7), 657 (2017)CrossRef
30.
Zurück zum Zitat Myers, A., et al.: Im2calories: towards an automated mobile vision food diary. In: IEEE International Conference on Computer Vision (ICCV), pp. 1233–1241 (2015) Myers, A., et al.: Im2calories: towards an automated mobile vision food diary. In: IEEE International Conference on Computer Vision (ICCV), pp. 1233–1241 (2015)
31.
32.
Zurück zum Zitat Wang, Y., Liu, C., Zhu, F., Boushey, C.J., Delp, E.J.: Efficient superpixel based segmentation for food image analysis. In: IEEE International Conference on Image Processing (ICIP), pp. 2544–2548. IEEE (2016) Wang, Y., Liu, C., Zhu, F., Boushey, C.J., Delp, E.J.: Efficient superpixel based segmentation for food image analysis. In: IEEE International Conference on Image Processing (ICIP), pp. 2544–2548. IEEE (2016)
33.
Zurück zum Zitat Wang, Y., Zhu, F., Boushey, C.J., Delp, E.J.: Weakly supervised food image segmentation using class activation maps. In: IEEE International Conference on Image Processing (ICIP), pp. 1277–1281. IEEE (2017) Wang, Y., Zhu, F., Boushey, C.J., Delp, E.J.: Weakly supervised food image segmentation using class activation maps. In: IEEE International Conference on Image Processing (ICIP), pp. 1277–1281. IEEE (2017)
Metadaten
Titel
Evaluating CNN-Based Semantic Food Segmentation Across Illuminants
verfasst von
Gianluigi Ciocca
Davide Mazzini
Raimondo Schettini
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-13940-7_19