Large scale partial-duplicate image retrieval with bi-space quantization and geometric consistency

The state-of-the-art image retrieval approaches represent image with a high dimensional vector of visual words by quantizing local features, such as SIFT, solely in descriptor space. The resulting visual words usually suffer from the dilemma of discrimination and ambiguity. Besides, geometric relationships among visual words are usually ignored or only used for post-processing such as re-ranking. In this paper, to improve the discriminative power and reduce the ambiguity of visual word, we propose a novel bispace quantization strategy. Local features are quantized to visual words first in descriptor space and then in orientation space. Moreover, geometric consistency constraints are embedded into the relevance formulation. Experiments in web image search with a database of one million images show that our approach achieves an improvement of 65.4% over the baseline bag-of-words approach.