This paper considers the strategies of query expansion, relevance feedback and result fusion to increase both precision 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 the 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. Its F1-measure is 0.2791, which has 20.8% increase to the baseline model. In the content-based retrieval only experiments, the run without feedback outperforms the run with feedback. The latter has 10.84%, 9.13%, 20.46%, and 16.7% performance decrease in MAP, P20, CR20, and F1-measure. In the fusion experiment, integrating text-based and content-based retrieval not only reports more relevant images, but also more diverse ones. Its F1-measure is 0.3189.