Paper
19 November 2013 Bag-of-visual-ngrams for histopathology image classification
A. Pastor López-Monroy, Manuel Montes-y-Gómez, Hugo Jair Escalante, Angel Cruz-Roa, Fabio A. González
Author Affiliations +
Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 89220P (2013) https://doi.org/10.1117/12.2034113
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico
Abstract
This paper describes an extension of the Bag-of-Visual-Words (BoVW) representation for image categorization (IC) of histophatology images. This representation is one of the most used approaches in several high-level computer vision tasks. However, the BoVW representation has an important limitation: the disregarding of spatial information among visual words. This information may be useful to capture discriminative visual-patterns in specific computer vision tasks. In order to overcome this problem we propose the use of visual n-grams. N-grams based-representations are very popular in the field of natural language processing (NLP), in particular within text mining and information retrieval. We propose building a codebook of n-grams and then representing images by histograms of visual n-grams. We evaluate our proposal in the challenging task of classifying histopathology images. The novelty of our proposal lies in the fact that we use n-grams as attributes for a classification model (together with visual-words, i.e., 1-grams). This is common practice within NLP, although, to the best of our knowledge, this idea has not been explored yet within computer vision. We report experimental results in a database of histopathology images where our proposed method outperforms the traditional BoVWs formulation.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Pastor López-Monroy, Manuel Montes-y-Gómez, Hugo Jair Escalante, Angel Cruz-Roa, and Fabio A. González "Bag-of-visual-ngrams for histopathology image classification", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220P (19 November 2013); https://doi.org/10.1117/12.2034113
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Cited by 8 scholarly publications.
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KEYWORDS
Visualization

Image classification

Computer vision technology

Machine vision

Visual process modeling

Binary data

Information visualization

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