Increasing Relevance and Diversity in Photo Retrieval by Result Fusion

This paper considers the strategies of query expansion, relevance feedback and result fusion to increase both relevance and diversity in photo retrieval. In the text-based retrieval only experiments, the run with query expansion has better MAP and P20 than that without query expansion, and only has 0.85% decrease in CR20. Although relevance feedback run increases both MAP and P20, its CR20 decreases 10.18% compared with non-feedback run. It shows that relevance feedback brings in relevant but similar images, thus diversity may be decreased. The run with both query expansion and relevance feedback is the best in the four text-based runs. In the content-based retrieval only experiments, the run without feedback outperforms the run with feedback. The latter has 10.84%, 9.13%, and 20.46% performance decrease in MAP, P20, and CR20. In the fusion experiment, integrating text-based and content-based retrieval not only reports more relevant images, but also more diverse ones.