Weighted visual vocabulary to balance the descriptive ability on general dataset

Many image retrieval and search systems take visual words as the key to represent images. Extracted feature descriptors are assigned to best matching visual words in the vocabulary. However, the visual word based retrieval method is not satisfactory in real applications as the descriptive ability of visual vocabulary is not strong enough to describe image local features. In this paper, we investigate on the problem of the descriptive ability of visual vocabulary. There are mainly two contributions in this paper. Firstly, we make a comprehensive analysis on the retrieval performance of standard bag-of-features (BOF) method by using different codebooks. The codebook is generated on one dataset and used for retrieval on the same dataset and on different datasets, which is called “homology codebook” and “non-homology codebook”, respectively. Experimental results show that there exists performance drop when a non-homology codebook is used for retrieval on one dataset, compared to the homology codebook. This phenomena is usually neglected by most of the retrieval tasks and systems. Secondly, in order to abate the influence of non-homology codebook, a weighting based method is proposed to balance the descriptive ability of codebook for different datasets. Experimental evaluations show that the proposed method outperforms standard BOF method, and it can prevent the performance drop to some extent when non-homology codebooks are used.