Exploiting visual saliency for increasing diversity of image retrieval results

Diversification of search results allows for better and faster search, gaining knowledge about different perspectives and viewpoints on retrieved information sources. Recently various methods for diversification of image retrieval results have been proposed, mainly using textual information or techniques imported from the natural language processing domain. However, images contain much more information than their textual descriptions and the use of visual features deserves special attention in this context. Visual saliency provides information about parts of the image perceived as most important, which are instinctively targeted by humans when shooting a photo or looking at a picture. For this reason we propose to exploit such information to improve diversification of search results. To this purpose, we introduce a saliency-based method to re-rank the results of a query and we show that it can achieve significantly better performances as compared to the baseline approach. Experimental validation conducted on a number of queries applied to various datasets demonstrates the potential of the use of saliency information for the diversification of image retrieval results.