High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multiview object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.
A multi-view object image search method has been proposed in REF , the authors focus on achieving higher visual search performance by leveraging the user interaction potential of mobile devices, and provide a comprehensive analysis of early and late fusion strategies for multi-image and multi-view queries on the single view object image dataset.
Mobile multi-view object image search
Multimedia Tools and Applications
Multimedia Tools and Applications
Computer Science