Browsing by Subject "Mobile visual search"

Now showing 1 - 3 of 3

Open Access
Mobile image search using multi-image queries
(2015-08) Çalışır, Fatih
Visual search has evolved over the years, according to the demand of users. Single image query search systems are inadequate to represent a query object, because they are limited to a single view of the object. Therefore, multi image query search systems have gained importance to increase search performance. We propose a mobile multi-image search system that makes use of local features and bag-of-visual-words (BoVW ) approach. In order to represent the query object better, we combine multiple local features each describing a different aspect of the query image. Employing different features in search improves the performance of the image search system. We also increase the retrieval performance using multi-view query approach together with fusion methods. Using multi-view images provides more comprehensive representation of the query image. We also develop a new multi-view object image database (MVOD), with the aim of evaluating the performance impact of using multi-view database images. Multi-view database images from different views and distances increase the possibility to match the query images to database images. As a result, using multi-view database images increases the precision of our search system. We compare our image search system with a state-of-the-art work in terms of average precision. In our experiments, we use single and multi image queries together with single viewed database. The results show that our image search system performs better with both single and multi image queries. We also performed experiments using MVOD database and show that using a multi-view database increases the precision.
Open Access
Mobile image search using multi-query images
(IEEE, 2015) Çalışır, Fatih; Bastan, M.; Güdükbay, Uğur; Ulusoy, Özgür
Recent advances in mobile device technology have turned the mobile phones into powerfull devices with high resolution cameras and fast processing capabilities. Having more user interaction potential compared to regular PCs, mobile devices with cameras can enable richer content-based object image queries: the user can capture multiple images of the query object from different viewing angles and at different scales, thereby providing much more information about the object to improve the retrieval accuracy. The goal of this paper is to improve the mobile image retrieval performance using multiple query images. To this end, we use the well-known bag-of-visual-words approach to represent the images, and employ early and late fusion strategies to utilize the information in multiple query images. With extensive experiments on an object image dataset with a single object per image, we show that multi-image queries result in higher average precision performance than single image queries. © 2015 IEEE.
Open Access
Mobile multi-view object image search
(Springer New York LLC, 2017) Çalışır, F.; Baştan M.; Ulusoy, Özgür; Güdükbay, Uğur
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 multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search. © 2016, Springer Science+Business Media New York.