Pervasive services often rely on multi-modal classification to implement situation-recognition capabilities. However, current classifiers are still inaccurate and unreliable. In this paper we present preliminary results obtained with a novel approach that combines well established classifiers using a commonsense knowledge base. The approach maps classification labels produced by independent classifiers to concepts organized within the Concept Net network. Then it verifies their semantic proximity by implementing a greedy approximate sub-graph search algorithm. Specifically, different classifiers are fused together on a commonsense basis for both: (i) improve classification accuracy and (ii) deal with missing labels. Experimental results are discussed through a real-world case study in which two classifiers are fused to recognize both user's activities and visited locations.

Improving Situation Recognition via Commonsense Sensor Fusion / Bicocchi, Nicola; Castelli, Gabriella; Mamei, Marco; Zambonelli, Franco. - STAMPA. - (2011), pp. 272-276. (Intervento presentato al convegno 2011 22nd International Workshop on Database and Expert Systems Applications, DEXA 2011 tenutosi a Toulouse, fra nel 29 - 31 August) [10.1109/DEXA.2011.43].

Improving Situation Recognition via Commonsense Sensor Fusion

BICOCCHI, Nicola;CASTELLI, Gabriella;MAMEI, Marco;ZAMBONELLI, Franco
2011

Abstract

Pervasive services often rely on multi-modal classification to implement situation-recognition capabilities. However, current classifiers are still inaccurate and unreliable. In this paper we present preliminary results obtained with a novel approach that combines well established classifiers using a commonsense knowledge base. The approach maps classification labels produced by independent classifiers to concepts organized within the Concept Net network. Then it verifies their semantic proximity by implementing a greedy approximate sub-graph search algorithm. Specifically, different classifiers are fused together on a commonsense basis for both: (i) improve classification accuracy and (ii) deal with missing labels. Experimental results are discussed through a real-world case study in which two classifiers are fused to recognize both user's activities and visited locations.
2011
2011 22nd International Workshop on Database and Expert Systems Applications, DEXA 2011
Toulouse, fra
29 - 31 August
272
276
Bicocchi, Nicola; Castelli, Gabriella; Mamei, Marco; Zambonelli, Franco
Improving Situation Recognition via Commonsense Sensor Fusion / Bicocchi, Nicola; Castelli, Gabriella; Mamei, Marco; Zambonelli, Franco. - STAMPA. - (2011), pp. 272-276. (Intervento presentato al convegno 2011 22nd International Workshop on Database and Expert Systems Applications, DEXA 2011 tenutosi a Toulouse, fra nel 29 - 31 August) [10.1109/DEXA.2011.43].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/738484
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