Affect based canonical image selection is suggested for summarizing a scene topic.

PAM based PLSA learning is proposed to transfer visual space into affective space.

Cluster-ranking model is presented to find a diverse set of representative images.

Thereafter, the high-ranked images are selected from the top-ranked clusters.

The experiments showed that the proposed method outperforms other baselines.