Abstract
The rapid development of digitization and data storage techniques resulted in images volume increase. In order to cope with this increasing amount of informations, it is necessary to develop tools to accelerate and facilitate the access to information and to ensure the relevance of information available to users. These tools must minimize the problems related to the image indexing used to represent content query information. In this paper, we present a new retrieval model called vectorization. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Oxford5k and Inria Holidays datasets to show the performance of our proposed system. (C) 2017 The Authors. Published by Elsevier B.V.