With explosive growth of digital images, content-based image retrieval has been emerged as an active research topic with many opportunities and challenges. Most of the visual search systems focus on the task of finding specific object, but a little at...
With explosive growth of digital images, content-based image retrieval has been emerged as an active research topic with many opportunities and challenges. Most of the visual search systems focus on the task of finding specific object, but a little attention has been paid for multiple object retrieval. In this paper, we present an efficient framework for processing multi-image queries, where the users can input multiple images with their objects of interest. We first propose a number of methods to encode image representation of queries. This encoded representation can be indexed further to retrieve an initial list of candidates. Moreover, we present the measuring scheme using Geometric Verification to estimate the similarity between query and candidate images during re-ranking stage. The experimental results performed on a benchmark dataset show that our approach provides promising performance.