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Ambiguous Proximity Distribution

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Intelligent Computing Methodologies (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8589))

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Abstract

Proximity Distribution Kernel is an effective method for bag-of-featues based image representation. In this paper, we investigate the soft assignment of visual words to image features for proximity distribution. Visual word contribution function is proposed to model ambiguous proximity distributions. Three ambiguous proximity distributions is developed by three ambiguous contribution functions. The experiments are conducted on both classification and retrieval of medical image data sets. The results show that the performance of the proposed methods, Proximity Distribution Kernel (PDK), is better or comparable to the state-of-the-art bag-of-features based image representation methods.

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References

  1. Penatti, O., Silva, F., Valle, E., Gouet-Brunet, V., Torres, R.: Visual word spatial arrangement for image retrieval and classification. Pattern Recognition 47(2), 705–720 (2014)

    Article  Google Scholar 

  2. Chen, J., Feng, B., Xu, B.: Spatial similarity measure of visual phrases for image retrieval. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part II. LNCS, vol. 8326, pp. 275–282. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  3. Furuya, T., Ohbuchi, R.: Visual saliency weighting and cross-domain manifold ranking for sketch-based image retrieval. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part I. LNCS, vol. 8325, pp. 37–49. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  4. Sun, Q., Hu, F., Hao, Q.: Mobile target scenario recognition via low-cost pyroelectric sensing system: Toward a context-enhanced accurate identification. IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(3), 375–384 (2014)

    Article  Google Scholar 

  5. Sun, Q., Hu, F., Hao, Q.: Context awareness emergence for distributed binary pyroelectric sensors. In: 2010 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 162–167. IEEE (2010)

    Google Scholar 

  6. Wang, Y., Jiang, W., Agrawal, G.: Scimate: A novel mapreduce-like framework for multiple scientific data formats. In: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 443–450. IEEE (2012)

    Google Scholar 

  7. Wang, Y., Su, Y., Agrawal, G.: Supporting a light-weight data management layer over hdf5. In: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 335–342. IEEE (2013)

    Google Scholar 

  8. Su, Y., Wang, Y., Agrawal, G., Kettimuthu, R.: Sdquery dsi: integrating data management support with a wide area data transfer protocol. In: Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis, vol. 47. ACM (2013)

    Google Scholar 

  9. Xu, L., Zhan, Z., Xu, S., Ye, K.: Cross-layer detection of malicious websites. In: CODASPY, pp. 141–152 (2013)

    Google Scholar 

  10. Cui, S., Soh, Y.C.: Linearity indices and linearity improvement of 2-d tetralateral position-sensitive detector. IEEE Transactions on Electron Devices 57(9), 2310–2316 (2010)

    Article  Google Scholar 

  11. Xia, Y., Wan, S., Yue, L.: A new texture direction feature descriptor and its application in contentbased image retrieval. In: Proceedings of the 3rd International Conference on Multimedia Technology, ICMT 2013. LNEE, vol. 278, pp. 143–151 (2014)

    Google Scholar 

  12. Xia, Y., Wan, S., Yue, L.: Local spatial binary pattern: A new feature descriptor for content-based image retrieval. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 9069 (2014)

    Google Scholar 

  13. Guimaräes Pedronette, D., Almeida, J., Da, S., Torres, R.: A scalable re-ranking method for contentbased image retrieval. Information Sciences 265, 91–104 (2014)

    Article  MathSciNet  Google Scholar 

  14. Xie, B., Mu, Y., Song, M., Tao, D.: Random projection tree and multiview embedding for large-scale image retrieval. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part II. LNCS, vol. 6444, pp. 641–649. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  15. Qian, J., Yang, J., Zhang, N., Yang, Z.: Histogram of visual words based on locally adaptive regression kernels descriptors for image feature extraction. Neurocomputing 129, 516–527 (2014)

    Article  Google Scholar 

  16. Zhang, C., Liu, R., Qiu, T., Su, Z.: Robust visual tracking via incremental low-rank features learning. Neurocomputing 131, 237–247 (2014)

    Article  Google Scholar 

  17. Wu, F., Pai, H.T., Yan, Y.F., Chuang, J.: Clustering results of image searches by annotations and visual features. Telematics and Informatics 31(3), 477–491 (2014)

    Article  Google Scholar 

  18. Zhou, Y., Li, L., Zhao, T., Zhang, H.: Region-based high-level semantics extraction with cedd. In: 2010 2nd IEEE International Conference on Network Infrastructure and Digital Content, pp. 404–408. IEEE (2010)

    Google Scholar 

  19. Zhou, Y., Li, L., Zhang, H.: Adaptive learning of region-based plsa model for total scene annotation. arXiv preprint arXiv:1311.5590 (2013)

    Google Scholar 

  20. Li, X., Gao, J., Li, H., Yang, L., Srihari, R.K.: A multimodal framework for unsupervised feature fusion. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 897–902. ACM (2013)

    Google Scholar 

  21. Kong, X., Schunn, C.D.: Global vs. local information processing in visual/spatial problem solving: The case of traveling salesman problem. Cognitive Systems Research 8(3), 192–207 (2007)

    Article  Google Scholar 

  22. Kong, X., Schunn, C.D., Wallstrom, G.L.: High regularities in eye-movement patterns reveal the dynamics of the visual working memory allocation mechanism. Cognitive Science 34(2), 322–337 (2010)

    Article  Google Scholar 

  23. Yang, J., Wang, Y., Wang, H.: K., H., Wang, W., J., S.: Automatic objects removal for scene completion. In: The 33rd Annual IEEE International Conference on Computer Communications (INFOCOM 2014), Workshop on Security and Privacy in Big Data (2014)

    Google Scholar 

  24. Cho, W., Seo, S., Na, I., Kang, S.: Automatic images classification using HDP-GMM and local image features. In: Jeong, Y.-S., Park, Y.-H., Hsu, C.-H(R.), Park, J.J(J.H.) (eds.) Ubiquitous Information Technologies and Applications. LNEE, vol. 280, pp. 323–333. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  25. Javed, U., Riaz, M., Ghafoor, A., Ali, S., Cheema, T.: Mri and pet image fusion using fuzzy logic and image local features. The Scientific World Journal 2014 (2014)

    Google Scholar 

  26. Yang, F., Xu, Y.Y., Wang, S.T., Shen, H.B.: Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing 131, 113–123 (2014)

    Article  Google Scholar 

  27. Sun, Q., Wu, P., Wu, Y., Guo, M., Lu, J.: Unsupervised multi-level non-negative matrix factorization model: Binary data case. Journal of Information Security 3(4) (2012)

    Google Scholar 

  28. Mu, Y., Ding, W., Tao, D., Stepinski, T.F.: Biologically inspired model for crater detection. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2487–2494. IEEE (2011)

    Google Scholar 

  29. Pinto, P., Tome, A., Santos, V.: Visual detection of vehicles using a bag-of-features approach. In: Proceedings of the 2013 13th International Conference on Autonomous Robot Systems, ROBOTICA 2013 (2013)

    Google Scholar 

  30. Kranthi Kiran, M., ShyamVamsi, T.: Hand gesture detection and recognition using affine- shift, bag-of-features and extreme learning machine techniques. In: Satapathy, S.C., Udgata, S.K., Biswal, B.N. (eds.) FICTA 2013. AISC, vol. 247, pp. 181–187. Springer, Heidelberg (2014)

    Google Scholar 

  31. Yu, J., Jeon, M., Pedrycz, W.: Weighted feature trajectories and concatenated bag-of-features for action recognition. Neurocomputing 131, 200–207 (2014)

    Article  Google Scholar 

  32. Zagoris, K., Pratikakis, I., Antonacopoulos, A., Gatos, B., Papamarkos, N.: Distinction between handwritten and machine-printed text based on the bag of visual words model. Pattern Recognition 47(3), 1051–1062 (2014)

    Article  Google Scholar 

  33. Fabian, J., Pires, R., Rocha, A.: Visual words dictionaries and fusion techniques for searching people through textual and visual attributes. Pattern Recognition Letters 39(1), 74–84 (2014)

    Article  Google Scholar 

  34. van Gemert, Jan C. Veenman, C.J.S.A.W.G.J.M.: Visual word ambiguity. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(7), 1271–1283 (2010)

    Google Scholar 

  35. Fiel, S., Sablatnig, R.: Writer identification and writer retrieval using the fisher vector on visual vocabularies. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 545–549 (2013)

    Google Scholar 

  36. Bolovinou, A., Kotsiourou, C., Amditis, A.: Dynamic road scene classification: Combining motion with a visual vocabulary model. In: Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, pp. 1151–1158 (2013)

    Google Scholar 

  37. Wang, L., Elyan, E., Song, D.: Rebuilding visual vocabulary via spatial-temporal context similarity for video retrieval. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part I. LNCS, vol. 8325, pp. 74–85. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  38. Sun, Q., Ma, R., Hao, Q., Hu, F.: Space encoding based human activity modeling and situation perception. In: 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 183–186. IEEE (2013)

    Google Scholar 

  39. Sun, Q., Hu, F., Hao, Q.: Human activity modeling and situation perception based on fiber-optic sensing system. IEEE Transactions on Human Machine Systems (2014)

    Google Scholar 

  40. Sun, T., Ding, S., Xu, X.: No-reference image quality assessment through sift intensity. Applied Mathematics and Information Sciences 8(4), 1925–1934 (2014)

    Article  Google Scholar 

  41. Meng, X., Yin, Y., Yang, G., Xi, X.: Retinal identification based on an improved circular gabor filter and scale invariant feature transform. Sensors 13(7), 9248–9266 (2013)

    Article  Google Scholar 

  42. Travieso, C., Del Pozo-Banos, M., Alonso, J.: Fused intra-bimodal face verification approach based on scale-invariant feature transform and a vocabulary tree. Pattern Recognition Letters 36(1), 254–260 (2014)

    Article  Google Scholar 

  43. Li, Y., Liu, W., Li, X., Huang, Q., Li, X.: Ga-sift: A new scale invariant feature transform for multispectral image using geometric algebra. Information Sciences (2014)

    Google Scholar 

  44. Makar, M., Chang, C.L., Chen, D., Tsai, S., Girod, B.: Compression of image patches for local feature extraction. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, pp. 821–824 (2009)

    Google Scholar 

  45. Shih, H.C., Chuang, C.Y., Huang, C.L., Lin, C.H.: Gender classification using bayesian classifier with local binary patch features. In: Proceedings of IEEE 4th International Conference on Nonlinear Science and Complexity, NSC 2012, pp. 45–50 (2012)

    Google Scholar 

  46. Zhang, Q., Zhang, L., Yang, Y., Tian, Y., Weng, L.: Local patch discriminative metric learning for hyperspectral image feature extraction. IEEE Geoscience and Remote Sensing Letters (2013)

    Google Scholar 

  47. Chen, G., Sun, R., Ren, Z., Wang, Z., Sun, L.: Local shape patch based feature matching method. Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 44(suppl.2), 33–39 (2013)

    Google Scholar 

  48. Chandler, D., Field, D.: Estimates of the information content and dimensionality of natural scenes from proximity distributions. Journal of the Optical Society of America A: Optics and Image Science, and Vision 24(4), 922–941 (2007)

    Article  Google Scholar 

  49. Ling, H., Soatto, S.: Proximity distribution kernels for geometric context in category recognition (2007)

    Google Scholar 

  50. Deselaers, T., Keysers, D.N.H.: Features for image retrieval: an experimental comparison. Information Retrieval 11(2), 77–107 (2008)

    Article  Google Scholar 

  51. Ay, M., Kisi, O.: Modelling of chemical oxygen demand by using anns, anfis and k-means clustering techniques. Journal of Hydrology 511, 279–289 (2014)

    Article  Google Scholar 

  52. Wang, L., Pan, C.: Robust level set image segmentation via a local correntropy-based k-means clustering. Pattern Recognition 47(5), 1917–1925 (2014)

    Article  Google Scholar 

  53. Lin, C.H., Chen, C.C., Lee, H.L., Liao, J.R.: Fast k-means algorithm based on a level histogram for image retrieval. Expert Systems with Applications 41(7), 3276–3283 (2014)

    Article  Google Scholar 

  54. Ling, H.B., Soatto, S.: Proximity distribution kernels for geometric context in category recognition. In: 2007 IEEE 11th International Conference on Computer Vision, vol. 1-6, pp. 245–252 (2007)

    Google Scholar 

  55. Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(11), 2032–2047 (2009)

    Article  Google Scholar 

  56. Deselaers, T., et al.: Overview of the imageCLEF 2007 object retrieval task. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 445–471. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  57. Li, M., Li, Y., Huang, X., Zhao, G., Tian, W.: Evaluating growth models of pseudomonas spp. in seasoned prepared chicken stored at different temperatures by the principal component analysis (pca). Food Microbiology 40, 41–47 (2014)

    Article  Google Scholar 

  58. Lu, Y., Gao, B., Chen, P., Charles, D., Yu, L.: Characterisation of organic and conventional sweet basil leaves using chromatographic and flow-injection mass spectrometric (fims) fingerprints combined with principal component analysis. Food Chemistry 154, 262–268 (2014)

    Article  Google Scholar 

  59. Niu, Y., Wang, X.: On the k-nearest neighbor classifier with locally structural consistency. In: Liu, X., Ye, Y. (eds.) Proceedings of the 9th International Symposium on Linear Drives for Industry Applications, Volume 2. LNEE, vol. 271, pp. 269–277. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  60. Souza, R., Rittner, L., Lotufo, R.: A comparison between k-optimum path forest and k-nearest neighbors supervised classifiers. Pattern Recognition Letters 39(1), 2–10 (2014)

    Article  Google Scholar 

  61. Li, C.L., Wang, E., Huang, G.J., Chen, A.: Top-n query processing in spatial databases considering bi-chromatic reverse k-nearest neighbors. Information Systems 42, 123–138 (2014)

    Article  Google Scholar 

  62. Müller, H., Kalpathy-Cramer, J., Kahn Jr., C.E., Hatt, W., Bedrick, S., Hersh, W.: Overview of the imageCLEFmed 2008 medical image retrieval task. In: Peters, C., Deselaers, T., Ferro, N., Gonzalo, J., Jones, G.J.F., Kurimo, M., Mandl, T., Peñas, A., Petras, V. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 512–522. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  63. Shen, J., Su, P.-C., Cheung, S.-C., Zhao, J.: Virtual Mirror Rendering with Stationary RGB-D Cameras and Stored 3D Background. IEEE Transactions on Image Processing 22(9), 1–16 (2013)

    Article  MathSciNet  Google Scholar 

  64. Shen, J., Cheung, S.C.S.: Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2013, pp. 1187–1194. IEEE (2013)

    Google Scholar 

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Authors and Affiliations

  1. Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China

    Quanquan Wang & Yongping Li

  2. University of Chinese Academy of Sciences, Bejing, 100049, China

    Quanquan Wang

  3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China

    Quanquan Wang

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  1. Quanquan Wang
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  2. Yongping Li
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Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, 680–749, Ulsan, Korea

    Kang-Hyun Jo

  3. Department of Automation, Tsinghua University, Bejing, China

    Ling Wang

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Wang, Q., Li, Y. (2014). Ambiguous Proximity Distribution. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_42

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  • DOI: https://doi.org/10.1007/978-3-319-09339-0_42

  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-09339-0

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