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An novel approach to extract the content retrieval with the image perception using collaborative community oriented sifting (CCOS)

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Abstract

Today it is practically difficult to recover data with a catchphrase pursuit when the data is spread more than a few pages. Semantic Web is a growth of the present web in which data is given well characterized meaning and web personalization is the one of the utilization of the semantic web. In this we have investigated correlation of different community oriented sifting procedures and filtering methodologies were induced to get the exact result for the given queries. In our work we have proposed an image based content retrieval for the given search query from the enduser. The user identification are taken in to consideration to frame the search using the community oriented sifting mining for their desires and the same will be mapped with the image to give out the exact content in terms of text and images. Those systems are memory based, model based and cross breed communitarian sifting. Our review demonstrates that the execution of half and half communitarian separating method is superior to anything memory based and show based collective sifting strategy. We have introduced standardization step, which will enhance precision of conventional community oriented sifting procedures. One of the intense personalization advances controlling the versatile web is synergistic separating. Collaborative community oriented sifting (CCOS) is the way toward separating or assessing things through the sentiments of other individuals. CCOS innovation unites the suppositions of huge interconnected groups on the web, supporting separating of significant amounts of information.

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References

  1. Makadia, A., Pavlovic, V., Kumar, S.: Baselines for image annotation. Int. J. Comput. Vis. 90–1, 88–105 (2010). doi:10.1007/s11263-010-0338-6

    Article  Google Scholar 

  2. Yang, C., Dong, M., Fotouhi, F.: Region based image annotation through multiple-instance learning. In Proceedings of 13th ACM International Conference Multimedia, pp. 435–438. (2005). doi:10.1145/1101149.1101245

  3. Wang, C., Yan, S., Zhang, L., Zhang., H.-J.: Multi-label sparse coding for automatic image annotation. In Proceedings of IEEE International Conference Computer Vision Pattern Recognition, pp. 1643–1650 (2009). doi:10.1109/CVPR.2009.5206866

  4. Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29–3, 394–410 (2007). doi:10.1109/TPAMI.2007.61

    Article  Google Scholar 

  5. Wang, H., Huang, H., Ding, C.: Image annotation using multi-label correlated Green’s function. In Proceedings of IEEE 12th International Conference Computer Vision, pp. 2029–2034 (2009). doi:10.1109/ICCV.2009.5459447

  6. Han, J., Jo, J., Ji, H., Lim, H.: A collaborative recommender system for learning courses considering the relevance of a learner’s learning skills. Clust. Comput. 19–4, 2273–2284 (2016). doi:10.1007/s10586-016-0670-x

    Article  Google Scholar 

  7. Wu, J., Shen, H., Li, Y., Xiao, Z.-B., Lu, M.Y., Wang, C.-L.: Learning a hybrid similarity measure for image retrieval. Pattern Recognit. 46–11, 2927–2939 (2015). doi:10.1016/j.patcog.2013.04.008

    Article  MATH  Google Scholar 

  8. Tang, J., Hong, R., Yan, S., Chua, T.-S., Qi, G.-J., Jain, R.: Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images. ACM Trans. Intell. Syst. Technol. 2–2, 1–16 (2011). doi:10.1145/1899412.1899418

    Article  Google Scholar 

  9. Tang, J., Yan, S., Hong, R., Qi, G.-J. Chua, T.-S.: Inferring semantic concepts from community-contributed images and noisy tags. In Proceedings of 17th ACM International Conference Multimedia, pp. 223–232 (2009). doi:10.1145/1631272.1631305

  10. Wu, L., Jin, R., Jain, A.K.: Tag completion for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 716–727 (2015). doi:10.1109/TPAMI.2012.124

    Article  Google Scholar 

  11. Guillaumin, M., Mensink, T., Verbeek, J., Schmid, C.: TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation. In: Proceedings of IEEE 12th International Conference Computer Vision, pp. 309–316 (2009). doi:10.1109/ICCV.2009.5459266

  12. Manjula, R., Chilambuchelvan, A.: An approach for content retrieval from web pages using clustering techniques. Circuits Syst. 7, 2663–2675 (2016). doi:10.4236/cs.2016.79230

  13. Feng, Q.D., Xu, M., Zhang, X.: The model for improving big data sub-image retrieval performance using scalable vocabulary tree based on predictive clustering. Cluster Comput. 19–2, 699–708 (2016). doi:10.1007/s10586-016-0551

    Article  Google Scholar 

  14. Datta, R., D, Joshi, J, Li, JZ, Wang: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 5 (2008). doi:10.1145/1348246.1348248

    Article  Google Scholar 

  15. Zhang, S., Huang, J., Huang, Y., Yu, Y., Li, H., Metaxas, D. N.: Automatic image annotation using group sparsity. In Proceeding of IEEE International Conference Computer Vision Pattern Recognition, pp. 3312–3319 (2010). doi:10.1109/CVPR.2010.5540036

  16. Liu, W., Tao, D.: Multiview Hessian regularization for image annotation. IEEE Trans. Image Process. 22–7, 2676–2687 (2013). doi:10.1109/TIP.2013.2255302

    Article  MathSciNet  MATH  Google Scholar 

  17. Cai, X., Nie, F., Cai, W., Huang, H.: New graph structured sparsity model for multi-label image annotations. In Proceedings of IEEE International Conference Computer Vision, pp. 801-808 (2013). doi:10.1109/ICCV.2013.104

  18. Yang, Y., Wu, F., Nie, F., Shen, H.T., Zhuang, Y., Hauptmann, A.G.: Web and personal image annotation by mining label correlation with relaxed visual graph embedding. IEEE Trans. Image Process. 21–3, 1339–1351 (2012). doi:10.1109/TIP.2011.2169269

    Article  MathSciNet  MATH  Google Scholar 

  19. Verma, Y., Jawahar, C.V.: Image annotation using metric learning in semantic neighbourhoods. In Proceedings of 12th European Conference Computer Vision, pp. 836-849 (2012). doi:10.1007/978-3-642-33712-3_60

  20. Li, Y., McLean, D., Bandar, Z.A., O’Shea, J.D., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. IEEE Trans. Knowl. Data Eng. 18–8, 1138–1150 (2006). doi:10.1109/TKDE.2006.130

    Article  Google Scholar 

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Authors and Affiliations

  1. Anna University, Chennai, India

    R. Manjula

  2. Department of CSE, R.M.D Engineering College, Chennai, India

    A. Chilambuchelvan

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  1. R. Manjula
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  2. A. Chilambuchelvan
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Correspondence to R. Manjula.

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Manjula, R., Chilambuchelvan, A. An novel approach to extract the content retrieval with the image perception using collaborative community oriented sifting (CCOS). Cluster Comput 22 (Suppl 5), 10567–10575 (2019). https://doi.org/10.1007/s10586-017-1125-8

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  • DOI: https://doi.org/10.1007/s10586-017-1125-8

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