Affinity Propagation promoting Diversity in Visuo-entropic and Text Features for CLEF Photo Retrieval 2008 Campaign

We develop for the CLEF PHOTO 2008 task a new visual features using various pixel projections for training SVMs, allowing us to produce image retrieval and clustering using affinity propagation. To heighten the diversity of the top of the retrieval results, we put the images with the lowest rank in each cluster into the top. The LSIS run which used only the visual information is at the 6th best team rank in the AUTO IMG run type. For AUTO TXTIMG runs, we merge by simple harmonic or arithmetic average our visual ranks to the textual ranks of the LIG language model participating to the AVEIR consortium. Then we also perform the affinity propagation and the reranking on this TXTIMG run, which gives complementary information to the AVEIR consortium, helping in producing the third best AUTO TXTIMG run (after XEROX). We discuss on the clustering performance of the various run types, and then we give some perspectives for enhancing such diversity image retrieval system. If affinity propagation clustering seems efficient for promoting visual diversity, our results show that clustering process itself should merge independant textual and visual clustering informations.