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Iterative Random Visual Word Selection


In content based image retrieval, one of the most important step is the construction of image signatures. To do so, a part of state-of-the-art approaches propose to build a visual vocabulary. In this paper, we propose a new methodology for visual vocabulary construction that obtains high retrieval results. Moreover, it is computationally inexpensive to build and needs no prior knowledge on features or dataset used. Classically, the vocabulary is built by aggregating a certain number of features in centroids using a clustering algorithm. The final centroids are assimilated to visual "words". Our approach for building a visual vocabulary is based on an iterative random visual word selection mixing a saliency map and tf-idf scheme. Experiment results show that it outperforms the original "Bag of visual words" based approach in efficiency and effectiveness.
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hal-01287554 , version 1 (21-03-2016)



Thierry Urruty, Syntyche Gbehounou, Huu Ton Le, Jean Martinet, Christine Fernandez-Maloigne. Iterative Random Visual Word Selection. ICMR 2014 ACM International Conference on Multimedia Retrieval, Apr 2014, Glasgow, United Kingdom. ⟨10.1145/2578726.2578758⟩. ⟨hal-01287554⟩
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