Abstract
The recent shift in human-computer interaction from desktop to mobile computing fosters the needs of new interfaces for web image search results exploration. In order to leverage users’ efforts, we present a set of state-of-the-art ephemeral clustering algorithms, which allow to summarize web image search results into meaningful clusters. This way of presenting visual information on mobile devices is exhaustively evaluated based on two main criteria: clustering accuracy, which must be maximized, and wasted space-interface, which must be minimized. For the first case, we use a broad set of metrics to evaluate ephemeral clustering over a public golden standard data set of web images. For the second case, we propose a new metric to evaluate the mismatch of the used space-interface between the ground truth and the cluster distribution obtained by ephemeral clustering. The results evidence that there exist high divergences between clustering accuracy and used space maximization. As a consequence, the trade-off of cluster-based exploration of web image search results on mobile devices is difficult to define, although our study evidences some clear positive results.
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References
Kamvar, M., Baluja, S.: A large scale study of wireless search behavior: Google mobile search. In: 24th Annual SIGCHI Conference on Human Factors in Computing Systems, CHI (2006)
Kamvar, M., Kellar, M., Patel, R., Xu, Y.: Computers and iphones and mobile phones, oh my!: a logs-based comparison of search users on different devices. In: 18th International World Wide Web Conference (WWW), pp. 801–810 (2009)
André, P., Cutrell, E., Tan, D.S., Smith, G.: Designing Novel Image Search Interfaces by Understanding Unique Characteristics and Usage. In: Gross, T., Gulliksen, J., Kotzé, P., Oestreicher, L., Palanque, P., Prates, R.O., Winckler, M. (eds.) INTERACT 2009. LNCS, vol. 5727, pp. 340–353. Springer, Heidelberg (2009)
Carpineto, C., Romano, G.: Mobile information retrieval with search results clustering: Prototypes and evaluations. Journal of the American Society for Information Science 60, 877–895 (2009)
Ferragina, P., Gulli, A.: A personalized search engine based on web-snippet hierarchical clustering. Software: Practice and Experience 38(2), 189–225 (2008)
Carpineto, C., Romano, G.: Optimal meta search results clustering. In: 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 170–177 (2010)
Scaiella, U., Ferragina, P., Marino, A., Ciaramita, M.: Topical clustering of search results. In: 5th ACM International Conference on Web Search and Data Mining (WSDM), pp. 223–232 (2012)
Cai, D., He, X., Li, Z., Ma, W.Y., Wen, J.R.: Hierarchical clustering of www image search results using visual, textual and link information. In: 12th Annual ACM International Conference on Multimedia (MM), pp. 952–959 (2004)
Wang, X.J., He, Q.C., Li, X.: Grouping web image search result. In: 12th Annual ACM International Conference on Multimedia, MM (2004)
Ding, H., Liu, J., Lu, H.: Hierarchical clustering-based navigation of image search results. In: 16th Annual ACM International Conference on Multimedia (MM), pp. 741–744 (2008)
Liu, H., Xie, X., Tang, X., Ma, W.-Y.: Clustering-Based Navigation of Image Search Results on Mobile Devices. In: Myaeng, S.-H., Zhou, M., Wong, K.-F., Zhang, H.-J. (eds.) AIRS 2004. LNCS, vol. 3411, pp. 325–336. Springer, Heidelberg (2005)
Moreno, J.G., Dias, G.: Using ephemeral clustering and query logs to organize web image search results on mobile devices. In: 2011 International ACM Workshop on Interactive Multimedia on Mobile and Portable Devices (IMMPD), pp. 33–38 (2011)
Krapac, J., Moray, A., Verbeek, J., Jurie, F.: Improving web-image search results using query-relative classifiers. In: IEEE Conference on Computer Vision & Pattern Recognition (CVPR), pp. 1094–1101 (2010)
Zamir, O., Etzioni, O.: Web document clustering: A feasibility demonstration. In: 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 46–54 (1998)
Osinski, S., Stefanowski, J., Weiss, D.: Lingo: Search results clustering algorithm based on singular value decomposition. In: Intelligent Information Systems Conference (IIPWM), pp. 369–378 (2004)
Dias, G., Cleuziou, G., Machado, D.: Informative polythetic hierarchical ephemeral clustering. In: 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 104–111 (2011)
Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval 12(4), 461–486 (2009)
Wang, S., Jing, F., He, J., Du, Q., Zhang, L.: Igroup: Presenting web image search results in semantic clusters. In: 25th Annual SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 587–596 (2007)
Vitale, D., Ferragina, P., Scaiella, U.: Classification of Short Texts by Deploying Topical Annotations. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 376–387. Springer, Heidelberg (2012)
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Moreno, J.G., Dias, G. (2013). Using Text-Based Web Image Search Results Clustering to Minimize Mobile Devices Wasted Space-Interface. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_45
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DOI: https://doi.org/10.1007/978-3-642-36973-5_45
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