Uncertain Tagging of Images using Query Aware Determinization

Ms.Komal R. Ghuge


Determinizing probabilistic data is the problem that can be solved by enable such data to be stored in legacy system. legacy system is used to store the data and that system accepts only deterministic input. Probabilistic data may be generated by automated data analysis/enrichment technique such as information extraction. The goal is to generate a deterministic representation of probabilistic data to optimize the quality of the end-application that are built on deterministic data. To explore such a determinization problem in the context of two different data processing tasks such as triggers and selection queries. Some approaches such as thresholding or top-1 selection traditionally used for determinization lead to suboptimal performance for such applications. Instead, develop a query-aware strategy and show its advantages over existing solutions through a comprehensive empirical evaluation over real and synthetic datasets. The problem of determinizing probabilis- tic data is solved by workload of triggers/queries, and by finding the deterministic representation of the data that optimize certain qual- ity metrics of the answer to these triggers/queries. System solves the problem of determinization by minimizing the expected cost of the answer to queries. Efficient algorithm is used to optimize set-based quality metrics, such as F-measure. Correlations among tags can be leveraged in solutions to get better results.

Full Text:



Jie Xu, Sharad Mehrotra, Query Aware Determinization of Uncertain

Objects, IEEE Transactions on knowledge and data engineering, VOL.

, NO. 1, January 2015.

J. Li and J. Wang, Automatic linguistic indexing of pictures by a statistical

modeling approach, IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no.

, pp. 10751088, Sept. 2003.

C. Wangand, F. Jing, L. Zhang, and H. Zhang, Image annotation refinement

using random walk with restarts, in Proc. 14th Annu. ACM Int.

Conf. Multimedia, New York, NY, USA, 2006.

B. Minescu, G. Damnati, F. Bechet, and R. de Mori, Conditional use

of word lattices, confusion networks and 1-best string hypotheses in a

sequential interpretation strategy, in Proc. ICASSP, 2007.

Jian Pei, Ming Hua, Query Answering Techniques on Uncertain and

Probabilistic Data In VLDB, pages 11511154, 2006.

Umesh Gorela1, Bidita Hazarika2, Abhinesh Tiwari3, Priti Mithari,

Survey on Query Aware Strategy for Determining Uncertain Probabilistic

Data, in (IJSETR), Volume 4, Issue 10, October 2015 3510.

R. Nuray-Turan, D. V. Kalashnikov, S. Mehrotra, and Y. Yu, Attribute

and object selection queries on objects with probabilistic attributes, ACM

Trans. Database Syst., vol. 37, no. 1, Article 3, Feb. 2012.

V. Jojic, S. Gould, and D. Koller, Accelerated dual decompositionfor

MAP inference, in Proc. 27th ICML, Haifa, Israel, 2010.

D. Sontag, D. K. Choe, and Y. Li, Efficiently searching for frustrated

cycles in map inference, in Proc. 28th Conf. UAI, 2012.

] I. Bordino, C. Castillo, D. Donato, and A. Gionis, Query similarity by

projecting the query-flow graph, in Proc. 33rd Int. ACM SIGIR, Geneva,

Switzerland, 2010.

] P. Jhancy, K. Lakshmi,Dr.S. Prem Kumar, Query Aware Determinization

of Uncertain Objects in ijcert Volume 2, Issue 12, December-2015,

pp. 904-907 Patil et al., International Journal of Advanced Research in

Computer Science and Software Engineering 6(1), January - 2016, pp.


R. Nuray-Turan, D. V. Kalashnikov, S. Mehrotra, and Y. Yu, Attribute

and object selection queries on objects with probabilistic attributes, ACM

Trans. Database Syst., vol. 37, no. 1, Article 3, Feb. 2012.

B. Sigurbjornsson and R. V. Zwol, Flickr tag recommendation based on

collective knowledge, in Proc. 17th Int. Conf. WWW, New York, NY,

USA, 2008.

A. Rae, B. Sigurbjornsson, and R. V. Zwol, Improving tag recommendation

using social networks, in Proc. RIAO, Paris, France, 2010. [15]

D. Carmel et al., Static index pruning for information retrieval systems,

in Proc. 24th Annu. Int. ACM SIGIR, New Orleans, LA, USA, 2001.


Copyright © IJETT, International Journal on Emerging Trends in Technology