Aggregated Deep Feature from Activation Clusters for Particular Object Retrieval
Zhenfang Chen, Zhanghui Kuang, Kwan-Yee K. Wong, Wayne Zhang · 2017
This paper introduces a clustering based deep feature for particular object retrieval. Many object retrieval algorithms focus on aggregating local features into compact image representations. Recently proposed algorithms, such as R-MAC and its variants, aggregate maximum activations of convolutions from rectangular regions of multiple scales and have achieved state-of-the-art performance. Such rectangular regions, however, cannot fit the "non-rectangular" shape of an arbitrary object well, and therefore cover much clutter in the background. This paper targets at mitigating this problem by proposing a deep feature based on clustering the activations of convolutions and aggregating the maximum activations from such clusters. Compared with the square regions used in R-MAC, the clusters thus obtained can better fit the arbitrary shapes and sizes of the objects of interest. By not taking spatial location into account, it is possible to have a single cluster covering multiple disconnected regions that correspond to repeated but isolated visual patterns. This helps to avoid over-weighting such patterns in the aggregated feature. Experiments are carried out on the challenging Oxford5k and Paris6k datasets, and results show that our clustering based deep feature outperforms the R-MAC feature.