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Medical Image Retrieval using Bag of Meaningful Visual Words: Unsupervised visual vocabulary pruning with PLSA

Foncubierta-Rodríguez, Antonio and Garcia Seco De Herrera, Alba and Müller, Henning (2013) Medical Image Retrieval using Bag of Meaningful Visual Words: Unsupervised visual vocabulary pruning with PLSA. In: 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare MIIRH '13, 2013-10 - 2013-10-22, Barcelona Spain.

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Content--based medical image retrieval has been proposed as a technique that allows not only for easy access to images from the relevant literature and electronic health records but also for training physicians, for research and clinical decision support. The bag-of-visual-words approach is a widely used technique that tries to shorten the semantic gap by learning meaningful features from the dataset and describing documents and images in terms of the histogram of these features. Visual vocabularies are often redundant, over--complete and noisy. Larger than required vocabularies lead to high--dimensional feature spaces, which present important disadvantages with the curse of dimensionality and computational cost being the most obvious ones. In this work a visual vocabulary pruning technique is presented. It enormously reduces the amount of required words to describe a medical image dataset with no significant effect on the accuracy. Results show that a reduction of up to 90% can be achieved without impact on the system performance. Obtaining a more compact representation of a document enables multimodal description as well as using classifiers requiring low--dimensional representations.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Notes: keywords: Bag of visual words, language modelling, Medical image retrieval location: Barcelona, Spain
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 27 Jan 2020 10:03
Last Modified: 23 Sep 2022 19:02

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