Extended-bag-of-features for translation, rotation, and scale-invariant image retrieval
While bag-of-features (BOF) models have been widely applied for addressing image retrieval problems, the resulting performance is typically limited due to its disregard of spatial information of local image descriptors (and the associated visual words). In this paper, we present a novel spatial pooling scheme, called extended bag-of-features (EBOF), for solving the above task. Besides improving image representation capability, the incorporation of the our EBOF model with a proposed circular-correlation based similarity measure allows us to perform translation, rotation, and scale-invariant image retrieval. We conduct experiments on two benchmark image datasets, and the performance confirms the effectiveness and robustness of our proposed approach.