Efficient Bag-of-Feature kernel representation for image similarity search

Although “Bag-of-Features” image models have shown very good potential for object matching and image retrieval, such a complex data representation requires computationally expensive similarity measure evaluation. In this paper, we propose a framework unifying dictionary-based and kernel-based similarity functions that highlights the tradeoff between powerful data representation and eff cient similarity computation. On the basis of this formalism, we propose a new kernel-based similarity approach for Bag-of-Feature descriptions. We introduce a method for fast similarity search in large image databases. The conducted experiments prove that our approach is very competitive among State-of-the-art methods for similarity retrieval tasks.