A Benchmark Dataset to Study the Representation of Food Images

详细信息    查看全文

• 作者：Giovanni Maria Farinella (16)
  Dario Allegra (16)
  Filippo Stanco (16)
  
  16. Image Processing Laboratory ; Department of Mathematics and Computer Science ; University of Catania ; Catania ; Italy
  
• 关键词：Food dataset ; Food recognition ; Near duplicate image retrieval ; Textons ; PRICoLBP ; SIFT
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8927
• 期：1
• 页码：584-599
• 全文大小：1,153 KB
• 参考文献：1. Kong, F, Tan, J (2012) Dietcam: Automatic dietary assessment with mobile camera phones. Pervasive and Mobile Computing 8: pp. 147-163 CrossRef
  2. Xu, C., He, Y., Khannan, N., Parra, A., Boushey, C., Delp, E.: Image-based food volume estimation. In: International Workshop on Multimedia for Cooking and Eating Activities, pp. 75鈥?0 (2013)
  3. Kim, S., Schap, T.R., Bosch, M., Maciejewski, R., Delp, E.J., Ebert, D.S., Boushey, C.J.: Development of a mobile user interface for image-based dietary assessment. In: International Conference on Mobile and Ubiquitous Multimedia, pp. 1鈥?3 (2010)
  4. Arab, L., Estrin, D., Kim, D.H., Burke, J., Goldman, J.: Feasibility testing of an automated image-capture method to aid dietary recall (2011)
  5. Zhu, F, Bosch, M, Woo, I, Kim, S, Boushey, CJ, Ebert, DS, Delp, EJ (2010) The use of mobile devices in aiding dietary assessment and evaluation. Journal of Selected Topics in Signal Processing 4: pp. 756-766 CrossRef
  6. O鈥橪oughlin, G, Cullen, SJ, McGoldrick, A, O鈥機onnor, S, Blain, R, O鈥橫alley, S, Warrington, GD (2013) Using a wearable camera to increase the accuracy of dietary analysis. American Journal of Preventive Medicine 44: pp. 297-301 CrossRef
  7. Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., Yang, J.: Pfid: Pittsburgh fast-food image dataset. In: IEEE International Conference Image Processing, pp. 289鈥?92 (2009)
  8. Yang, S., Chen, M., Pomerleau, D., Sukthankar, R.: Food recognition using statistics of pairwise local features. In: IEEE Computer Vision and Pattern Recognition, pp. 2249鈥?256 (2010)
  9. Farinella, G.M., Moltisanti, M., Battiato, S.: Classifying food images represented as Bag of Textons. in: IEEE International Conference on Image Processing (ICIP), pp. 5212鈥?216 (2014)
  10. Oliveira, RD, Cherubini, M, Oliver, N (2010) Looking at near-duplicate videos from a human-centric perspective. ACM Transaction on Multimedia Comput. Commun. Appl. 6: pp. 15:1-15:22
  11. Ke, Y., Sukthankar, R., Huston, L.: Efficient near-duplicate detection and sub-image retrieval. In: ACM International Conference on Multimedia, pp. 869鈥?76 (2004)
  12. Hu, Y, Cheng, X, Chia, LT, Xie, X, Rajan, D, Tan, AH (2009) Coherent phrase model for efficient image near-duplicate retrieval. IEEE Transactions on Multimedia 11: pp. 1434-1445 CrossRef
  13. Varma, M, Zisserman, A (2005) A Statistical Approach to Texture Classification from Single Images. International Journal of Computer Vision 62: pp. 61-81 CrossRef
  14. Qi, X., Xiao, R., Guo, J., Zhang, L.: Pairwise rotation invariant co-occurrence local binary pattern. In: European Converence on Computer Vision, pp. 158鈥?71 (2012)
  15. Lowe, DG (2004) Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60: pp. 91-110 CrossRef
  16. Chen, H.C., Jia, W., Yue, Y., Li, Z., Sun, Y.N., Fernstrom, J.D., Sun, M.: Model-based measurement of food portion size for image-based dietary assessment using 3d/2d registration (2013)
  17. Jimnez, AR, Jain, AK, Ruz, RC, Rovira, JLP (1999) Automatic fruit recognition: a survey and new results using range/attenuation images. Pattern Recognition 32: pp. 1719-1736 CrossRef
  18. Joutou, T., Yanai, K.: A food image recognition system with multiple kernel learning. In: IEEE International Conference on Image Processing, pp. 285鈥?88 (2009)
  19. Matsuda, Y., Hoashi, H., Yanai, K.: Recognition of multiple-food images by detecting candidate regions. In: IEEE International Conference on Multimedia and Expo, pp. 25鈥?0 (2012)
  20. Julesz, B (1981) Textons, the elements of texture perception, and their interactions. Nature 290: pp. 91-97 CrossRef
  21. Malik, J, Belongie, S, Leung, T, Shi, J (2001) Contour and Texture Analysis for Image Segmentation. International Journal of Computer Vision 43: pp. 7-27 CrossRef
  22. Leung, T, Malik, J (2001) Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons. Int. J. Comput. Vision 43: pp. 29-44 CrossRef
  23. Battiato, S., Farinella, G.M., Gallo, G., Rav矛, D.: Exploiting textons distributions on spatial hierarchy for scene classification. Eurasip Journal on Image and Video Processing, pp. 1鈥?3 (2010)
  24. Ojala, T, Pietikainen, M, Maenpaa, T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24: pp. 971-987 CrossRef
  25. Qi, X., Xiao, R., Li, C., Qiao, Y., Guo, J., Tang, X.: Pairwise rotation invariant co-occurrence local binary pattern. IEEE Transactions on Pattern Analysis and Machine Intelligence (2014)
  26. Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, pp. 1150鈥?157 (1999)
  27. Brown, M, Lowe, D (2007) Automatic panoramic image stitching using invariant features. International Journal of Computer Vision 74: pp. 59-73 CrossRef
  28. Chum, O., Philbin, J., Zisserman, A.: Near duplicate image detection: min-hash and tf-idf weighting. In: British Machine Vision Conference, pp. 1鈥?0 (2008)
  29. Battiato, S., Farinella, G.M., Puglisi, G., Rav矛, R.: Aligning codebooks for near duplicate image detection. Multimedia Tools and Applications 72(2), 1483鈥?506 (2014)
  30. Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable libraryof computer vision algorithms (2008). http://www.vlfeat.org/
  31. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Conference on Computer Vision and Pattern Recognition (2007)
  32. Savarese, S., Winn, J., Criminisi, A.: Discriminative object class models of appearance and shape by correlatons. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2033鈥?040 (2006)
  
• 作者单位：Computer Vision - ECCV 2014 Workshops
• 丛书名：978-3-319-16198-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

It is well-known that people love food. However, an insane diet can cause problems in the general health of the people. Since health is strictly linked to the diet, advanced computer vision tools to recognize food images (e.g. acquired with mobile/wearable cameras), as well as their properties (e.g., calories), can help the diet monitoring by providing useful information to the experts (e.g., nutritionists) to assess the food intake of patients (e.g., to combat obesity). The food recognition is a challenging task since the food is intrinsically deformable and presents high variability in appearance. Image representation plays a fundamental role. To properly study the peculiarities of the image representation in the food application context, a benchmark dataset is needed. These facts motivate the work presented in this paper. In this work we introduce the UNICT-FD889 dataset. It is the first food image dataset composed by over \(800\) distinct plates of food which can be used as benchmark to design and compare representation models of food images. We exploit the UNICT-FD889 dataset for Near Duplicate Image Retrieval (NDIR) purposes by comparing three standard state-of-the-art image descriptors: Bag of Textons, PRICoLBP and SIFT. Results confirm that both textures and colors are fundamental properties in food representation. Moreover the experiments point out that the Bag of Textons representation obtained considering the color domain is more accurate than the other two approaches for NDIR.