DocumentCode :
1722938
Title :
Re-ranking by Multi-feature Fusion with Diffusion for Image Retrieval
Author :
Fan Yang ; Matei, Bogdan ; Davis, Larry S.
Author_Institution :
Univ. of Maryland, College Park, MD, USA
fYear :
2015
Firstpage :
572
Lastpage :
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.
Keywords :
Markov processes; graph theory; image denoising; image fusion; image retrieval; probability; diffusion process; feature-specific image similarity; image retrieval; mixture Markov model; multifeature fusion; multifeature information fusion; noise reduction; pair wise similarity scores; probabilistic model; reranking algorithm; undirected graph; Computer vision; Conferences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on
Conference_Location :
Waikoloa, HI
Type :
conf
DOI :
10.1109/WACV.2015.82
Filename :
7045936
Link To Document :
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