document: Visual diversification of image search results Due to the reliance on the textual information associated with an image , image search engines on the Web lack the discriminative power to deliver visually diverse search results . The textual descriptions are key to retrieve relevant results for a given user query , but at the same time provide little information about the rich image content . In this paper we investigate three methods for visual diversification of image search results . The methods deploy lightweight clustering techniques in combination with a dynamic weighting function of the visual features , to best capture the discriminative aspects of the resulting set of images that is retrieved . A representative image is selected from each cluster , which together form a diverse result set . Based on a performance evaluation we find that the outcome of the methods closely resembles human perception of diversity , which was established in an extensive clustering experiment carried out by human assessors .
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