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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 172–187Cite as

PicMarker: Data-Driven Image Categorization Based on Iterative Clustering

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10114))

Abstract

Facing the explosive growth of personal photos, an effective classification tool is becoming an urgent need for users to categorize images efficiently with personal preferences. As previous researches mainly focus on the accuracy of automatic classification within the pre-defined label space, they cannot be used directly for the personalized categorization. In this paper, we propose a data-driven classification method for personalized image classification tasks which can categorize images group by group. Firstly, we describe images from both the view of appearance and the view of semantic. Then, an iterative framework which incorporates spectral clustering with user intervention is utilized to categorize images group by group. To improve the quality of clustering, we propose an online multi-view metric learning algorithm to learn the similarity metrics in accordance with user’s criterion, and constraint propagation is integrated to adjust the similarity matrix. In addition, we build a system named PicMarker based on the proposed method. Experimental results demonstrate the effectiveness of the proposed method.

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References

  1. Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Good practice in large-scale learning for image classification. IEEE Trans. Pattern Anal. Mach. Intell. 36, 507–520 (2014)

    Article  Google Scholar 

  2. Ballan, L., Bertini, M., Uricchio, T., Bimbo, A.D.: Data-driven approaches for social image and video tagging. Multimedia Tools Appl. 74, 1443–1468 (2015)

    Article  Google Scholar 

  3. Bergamo, A., Torresani, L., Fitzgibbon, A.: PICODES: learning a compact code for novel-category recognition. In: NIPS, pp. 2088–2096 (2011)

    Google Scholar 

  4. Bianco, S., Ciocca, G., Cusano, C.: CURL: image classification using co-training and unsupervised representation learning. Comput. Vis. Image Underst. 145, 15–29 (2016)

    Article  Google Scholar 

  5. Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 11, 11–14 (2009)

    MathSciNet  MATH  Google Scholar 

  6. Cheng, M.M., Zhang, Z., Lin, W.Y., Torr, P.: BING: binarized normed gradients for objectness estimation at 300fps. In: CVPR, pp. 3286–3293. IEEE (2014)

    Google Scholar 

  7. Ciocca, G., Cusano, C., Santini, S., Schettini, R.: On the use of supervised features for unsupervised image categorization: an evaluation. Comput. Vis. Image Underst. 122, 155–171 (2014)

    Article  Google Scholar 

  8. Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What does classifying more than 10,000 image categories tell us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 71–84. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15555-0_6

    Chapter  Google Scholar 

  9. Elhoseiny, M., Saleh, B., Elgammal, A.: Write a classifier: zero-shot learning using purely textual descriptions. In: ICCV, pp. 2584–2591. IEEE (2013)

    Google Scholar 

  10. Feng, Z., Jin, R., Jain, A.: Large-scale image annotation by efficient and robust kernel metric learning. In: ICCV, pp. 1609–1616 (2013)

    Google Scholar 

  11. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  12. Csurka, G., Dance, C.R., Fan, L., Jutta Willamowski, C.B.: Visual categorization with bags of keypoints. In: ECCV Workshop, pp. 1–22 (2004)

    Google Scholar 

  13. Galleguillos, C., McFee, B., Lanckriet, G.R.G.: Iterative category discovery via multiple kernel metric learning. Int. J. Comput. Vision 108, 115–132 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Jin, R., Hoi, S.C.H., Yang, T.: Online multiple kernel learning: algorithms and mistake bounds. In: Hutter, M., Stephan, F., Vovk, V., Zeugmann, T. (eds.) ALT 2010. LNCS (LNAI), vol. 6331, pp. 390–404. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16108-7_31

    Chapter  Google Scholar 

  15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1–9 (2012)

    Google Scholar 

  16. Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36, 453–465 (2014)

    Article  Google Scholar 

  17. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178. IEEE (2006)

    Google Scholar 

  18. Lee, Y.J., Grauman, K.: Learning the easy things first: self-paced visual category discovery. In: CVPR, pp. 1721–1728. IEEE (2011)

    Google Scholar 

  19. Lee, Y.J., Grauman, K.: Object-graphs for context-aware visual category discovery. IEEE Trans. Pattern Anal. Mach. Intell. 34, 346–358 (2012)

    Article  Google Scholar 

  20. Li, L.J., Fei-Fei, L.: What, where and who? Classifying events by scene and object recognition. In: ICCV, pp. 1–8. IEEE (2007)

    Google Scholar 

  21. Lu, Z., Ip, H.H.S.: Constrained spectral clustering via exhaustive and efficient constraint propagation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 1–14. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15567-3_1

    Chapter  Google Scholar 

  22. Mensink, T., Verbeek, J., Perronnin, F., Csurka, G.: Distance-based image classification: generalizing to new classes at near zero cost. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1–14 (2013)

    Article  Google Scholar 

  23. Naphade, M., Smith, J., Tesic, J., Chang, S.-F., Hsu, W., Kennedy, L., Hauptmann, A., Curtis, J.: Large-scale concept ontology for multimedia. IEEE Multimedia 13, 86–91 (2006)

    Article  Google Scholar 

  24. Oliva, A., Hospital, W., Ave, L., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42, 145–175 (2001)

    Article  MATH  Google Scholar 

  25. Park, S.H., Yun, I.D., Lee, S.U.: Data-driven interactive 3D medical image segmentation based on structured patch model. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 196–207. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38868-2_17

    Chapter  Google Scholar 

  26. Ristin, M., Guillaumin, M., Gall, J., Gool, L.V.: Incremental learning of NCM forests for large-scale image classification. In: CVPR, pp. 3654–3661. IEEE (2014)

    Google Scholar 

  27. Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., Schiele, B.: What helps where - and why? Semantic relatedness for knowledge transfer. In: CVPR, pp. 910–917. IEEE (2010)

    Google Scholar 

  28. Royer, A., Lampert, C.H.: Classifier adaptation at prediction time. In: CVPR, pp. 1401–1409. IEEE (2015)

    Google Scholar 

  29. Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vision 105, 222–245 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  30. Li, X., Guo, Y., Schuurmans, D.: Semi-supervised zero-shot classification with label representation learning. In: ICCV, pp. 4211–4219. IEEE (2015)

    Google Scholar 

  31. Lu, Z., Ip, H.: Combining context, consistency, and diversity cues for interactive image categorization. IEEE Trans. Multimedia 12, 194–203 (2010)

    Article  Google Scholar 

  32. Su, Y., Jurie, F.: Learning compact visual attributes for large-scale image classification. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012. LNCS, vol. 7585, pp. 51–60. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33885-4_6

    Chapter  Google Scholar 

  33. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR, pp. 1–9. IEEE (2015)

    Google Scholar 

  34. Torresani, L., Szummer, M., Fitzgibbon, A.: Efficient object category recognition using classemes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 776–789. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15549-9_56

    Chapter  Google Scholar 

  35. Wang, M., Lai, Y.K., Liang, Y., Martin, R.R., Hu, S.M.: BiggerPicture: data-driven image extrapolation using graph matching. ACM Trans. Graph. 33, 1–13 (2014)

    Google Scholar 

  36. Xu, X., Shimada, A., Nagahara, H., Taniguchi, R.: Learning multi-task local metrics for image annotation. Multimedia Tools Appl. 75, 2203–2231 (2014)

    Article  Google Scholar 

  37. Ye, Z., Liu, P., Tang, X., Zhao, W.: May the torcher light our way: a negative-accelerated active learning framework for image classification. In: ICIP, pp. 1658–1662. IEEE (2015)

    Google Scholar 

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Acknowledgment

This work is supported by National High Technology Research and Development Program of China (No. 2007AA01Z334), National Natural Science Foundation of China (No. 61321491, 61272219), Innovation Fund of State Key Laboratory for Novel Software Technology (No. ZZKT2013A12, ZZKT2016A11), Program for New Century Excellent Talents in University of China (NCET-04-04605).

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China

    Jiagao Hu, Zhengxing Sun, Bo Li & Shuang Wang

Authors
  1. Jiagao Hu
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  2. Zhengxing Sun
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  3. Bo Li
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  4. Shuang Wang
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Correspondence to Zhengxing Sun .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Hu, J., Sun, Z., Li, B., Wang, S. (2017). PicMarker: Data-Driven Image Categorization Based on Iterative Clustering. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10114. Springer, Cham. https://doi.org/10.1007/978-3-319-54190-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-54190-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54189-1

  • Online ISBN: 978-3-319-54190-7

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