The Community for Technology Leaders
2015 IEEE Winter Conference on Applications of Computer Vision (WACV) (2015)
Waikoloa, HI, USA
Jan. 5, 2015 to Jan. 9, 2015
ISBN: 978-1-4799-6683-7
pp: 572-579
ABSTRACT
We present a re-ranking algorithm for image retrieval by fusing multi-feature information. We utilize pair wise similarity scores between images to exploit the underlying relationships among images. The initial ranked list for a query from each feature is represented as an undirected graph, where edge strength comes from feature-specific image similarity. Graphs from multiple features are combined by a mixture Markov model. In addition, we utilize a probabilistic model based on the statistics of similarity scores of similar and dissimilar image pairs to determine the weight for each graph. The weight for a feature is query specific, where the ranked lists of different queries receive different weights. Our approach for calculating weights is data-driven and does not require any learning. A diffusion process is then applied to the fused graph to reduce noise and achieve better retrieval performance. Experiments demonstrate that our approach significantly improves performance over baseline methods and outperforms many state-of-the-art retrieval methods.
INDEX TERMS
Conferences, Computer vision
CITATION

F. Yang, B. Matei and L. S. Davis, "Re-ranking by Multi-feature Fusion with Diffusion for Image Retrieval," 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2015, pp. 572-579.
doi:10.1109/WACV.2015.82
92 ms
(Ver 3.3 (11022016))