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Bag-of-Visual-Ngrams for Histopathology Image Classification
| Content Provider | Semantic Scholar |
|---|---|
| Author | López-Monroy, Adrián Pastor Montes--Gómez, Manuel Escalante, Hugo Jair Cruz-Roa, Angel González, Fabio A. |
| Copyright Year | 2013 |
| Abstract | This paper describes an extension of the Bag-of-Visual-Words (BoVW) representation for image classification (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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ccc.inaoep.mx/~mmontesg/publicaciones/2013/BagOfVisualNgramsForHistopathologyImageClassification-SIPAIM13.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm BMP file format Bag-of-words model in computer vision Classification Codebook D little i super little b Ag:PrThr:Pt:RBC^BPU:Ord Feature selection Grams High- and low-level Histopathology Image retrieval Information retrieval Language model Little man computer Machine learning Multimodal interaction N-gram Natural language processing Optic Nerve Glioma, Childhood Radiology Scientific literature Text mining University of California at Santa Cruz gram |
| Content Type | Text |
| Resource Type | Article |
