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Building Implicit Dictionaries based on Extreme Random Clustering for Modality Recognition

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Abstract

Introduced as a new subtask of the ImageCLEF 2010 challenge, we aim at recognizing the modality of a medical image based on its content only. Therefore, we propose to rely on a representation of images in terms of words from a visual dictionary. To this end, we introduce a very fast approach that allows the learning of implicit dictionaries which permits the construction of compact and discriminative bag of visual words. Instead of a unique computation-ally expensive clustering to create the dictionary, we propose a multiple random partitioning method based on Extreme Random Subspace Projection Ferns. By concatenating these multiple partitions, we can very efficiently create an implicit global quantization of the feature space and build a dictionary of visual words. Taking advantages of extreme randomization, our approach achieves very good speed performance on a real medical database, and this for a better accuracy than K-means clustering.
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Dates and versions

hal-01690325 , version 1 (22-01-2018)

Identifiers

  • HAL Id : hal-01690325 , version 1

Cite

Olivier Pauly, Diana Mateus, Nassir Navab. Building Implicit Dictionaries based on Extreme Random Clustering for Modality Recognition. Medical Content-Based Retrieval for Clinical Decision Support MCBR-CDS 2011, Sep 2011, Toronto, Canada. ⟨hal-01690325⟩
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