Combination of features through weighted ensembles for image classification
| Autor: | Humberto Bustince, Juan I. Forcen, Miguel Pagola, Edurne Barrenechea |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Contextual image classification Artificial neural network Computer science business.industry Feature vector Pattern recognition 02 engineering and technology Field (computer science) Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation Discriminative model Simple (abstract algebra) Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Software |
| Zdroj: | Applied Soft Computing. 84:105698 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2019.105698 |
| Popis: | Image classification is a multi-class problem that is usually tackled with ensembles of binary classifiers. Furthermore, one of the most important challenges in this field is to find a set of highly discriminative image features for reaching a good performance in image classification. In this work we propose to use weighted ensembles as a method for feature combination. First, a set of binary classifiers are trained with a set of features and then, the scores are weighted with distances obtained from another set of feature vectors. We present two different approaches to weight the score vector: (1) directly multiplying each score by the weights and (2) fusing the scores values and the distances through a Neural Network. The experiments have shown that the proposed methodology improves classification accuracy of simple ensembles and even more it obtains similar classification accuracy than state-of-the-art methods, but using much less parameters. |
| Databáze: | OpenAIRE |
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