Towards More Efficient Image Web Search

Mohammed Abdel Razek

Deanship of E-Learning and Distance Education, King Abdul-Aziz University Mathematics and Computer Science Department, Faculty of Science, Azhar University, Cairo, Egypt.

DOI: 10.4236/iim.2013.56022                

Abstract

With the flood of information on the Web, it has become increasingly necessary for users to utilize automated tools in order to find, extract, filter, and evaluate the desired information and knowledge discovery. In this research, we will present a preliminary discussion about using the dominant meaning technique to improve Google Image Web search engine. Google search engine analyzes the text on the page adjacent to the image, the image caption and dozens of other factors to determine the image content. To improve the results, we looked for building a dominant meaning classification model. This paper investigated the influence of using this model to retrieve more efficient images, through sequential procedures to formulate a suitable query. In order to build this model, the specific dataset related to an application domain was collected; K-means algorithm was used to cluster the dataset into K-clusters, and the dominant meaning technique is used to construct a hierarchy model of these clusters. This hierarchy model is used to reformulate a new query. We perform some experiments on Google and validate the effectiveness of the proposed approach. The proposed approach is improved for in precision, recall and F₁-measure by 57%, 70%, and 61% respectively.

Keywords

Web Mining; Image Retrieval; Dominant Meaning Technique; K-Means Algorithm, Web Search

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	X. Wang, S. Qiu, K. Liu and X. Tang, “Web Image Re-Ranking Using Query-Specific Semantic Signatures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, No. 99, 2013, pp. 1-14.
[2]	S. Sujatha and A. S. Sona, “New Fast K-Means Clustering Algorithm Using Modified Centroid Selection Method,” International Journal of Engineering Research & Technology (IJERT), Vol. 2, No. 2, 2013, pp. 1-9.
[3]	C. Zhang and S. X. Xia, “K-Means Clustering Algorithm with Improved Initial Center,” 2nd International Workshop on Knowledge Discovery and Data Mining (WKDD), Moscow, 23-25 January 2009, pp. 790-792.
[4]	M. Gautam and A. Xavier, “Speed Improvements to Information Retrieval-Based Dynamic Time Warping Using Hierarchical K-Means Clustering,” 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, 26-31 May 2013, pp. 8515-8519.
[5]	D. Mavroeidis and P. Magdalinos, “A Sequential Sampling Framework for Spectral k-Means Based on Efficient Bootstrap Accuracy Estimations: Application to Distributed Clustering,” ACM Transactions on Knowledge Discovery from Data, Vol. 7, No. 2, 2012, pp. 2-7.
[6]	J. Wu, H. Xiong and J. Chen, “Adapting the Right Measures for k-Means Clustering,” Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, 28 June-1 July 2009,, pp. 877-886.
[7]	M. A. Razek, C. Frasson and M. Kaltenbach, “Dominant Meanings towards Individualized Web Search for Learning Environments,” In: G. D. Magoulas and S. Y. Chen, Eds., Advances in Web-Based Education: Personalized Learning Environments, IDEA Group Publishing, Hershey, 2006.
[8]	Y. Jing, M. Covell, D. Tsai and J. M. Rehg, “Learning Query-Specific Distance Functions for Large-Scale Web Image Search,” IEEE Transactions on Multimedia, Vol. 15, No. 8, 2013, pp. 2022-2034.
[9]	G. Maderlechner, J. Panyr and P. Suda, “Finding Captions in PDF-Webpages for Semantic Annotations of Images,” In: D.-Y. Yeung, et al., Eds., Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science Volume, Springer-Verlag Berlin Heidelberg, 2006, pp. 422-430.
[10]	K. Hammouda, “Web Ming Dataset,” 2013. http://pami.uwaterloo.ca/~hammouda/webdata
[11]	P. Singh, R. H. Goudar, R. Rathore, A. Srivastav and S. Rao, “Domain Ontology Based Efficient Image Retrieval,” 7th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, 4-5 January 2013, pp. 445-452. http://dx.doi.org/10.1109/ISCO.2013.6481196
[12]	D. Gowsikhaa, S. Abirami and R. Baskaran, “Construction of Image Ontology Using Low-Level Features for Image Retrieval,” International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 10-12 January 2012, pp. 1-7. http://dx.doi.org/10.1109/ICCCI.2012.6158922
[13]	B. T. Sampath Kumar and J. N. Prakash, “Precision and Relative Recall of Search Engines: A Comparative Study of Google and Yahoo,” Singapore Journal of Library & Information Management, Vol. 38, No. 1, 2009, pp. 124-137.
[14]	S. M. Shafi and R. A. Rather, “Precision and Recall of Five Search Engines for Retrieval of Scholarly Information in the Field of Biotechnology,” Webology, Vol. 2, No. 2, 2005, pp. 42-47. http://www.webology.ir/2005/v2n2/a12.html
[15]	M. Gagnon, A. Zouaq and L. Jean-Louis, “Can We Use Linked Data Semantic Annotators for the Extraction of Domain-Relevant Expressions?” The International World Wide Web Conference Committee (IW3C2), WWW 2013 Companion, Rio de Janeiro, 13-17 May 2013, pp. 1239-1246.