This paper reports our work on object recognition by using the spatial pooler of Hierarchical Temporal Memory (HTM) as a method for feature selection. To perform the recognition task, we employed this pooling method to select features from COIL-100 dataset. We bench-marked the results with state-of-the-art feature extraction methods while using different amounts of training data (from 5% to 45%). The results indicate that the performed method is effective for object recognition with a low amount of training data in which state-of-the-art feature extraction methods show limitations.

Spatial pooling as feature selection method for object recognition

Kirtay M.;Vannucci L.;Albanese U.;Ambrosano A.;Falotico E.;Laschi C.
2018

Abstract

This paper reports our work on object recognition by using the spatial pooler of Hierarchical Temporal Memory (HTM) as a method for feature selection. To perform the recognition task, we employed this pooling method to select features from COIL-100 dataset. We bench-marked the results with state-of-the-art feature extraction methods while using different amounts of training data (from 5% to 45%). The results indicate that the performed method is effective for object recognition with a low amount of training data in which state-of-the-art feature extraction methods show limitations.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11382/530005
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