Using Text-Based Web Image Search Results Clustering to Minimize Mobile Devices Wasted Space-Interface

Abstract

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.

Query log driven web search results clustering

“…The underlying idea is that the number of clusters and the average number of Web snippets in each cluster approximate as much as possible the reference clustering. An illustration of this situation can be seen in [25] although the authors do not refer to this issue as an important one for SRC. In terms of implementation, we used the Java evaluator 13 to compute both F N 1 and ARI evaluation metrics, and the implementation provided by [3] 14 to compute F b 3 .…”