Automatic Date Fruit Recognition Using Outlier Detection Techniques and Gaussian Mixture Models
Aiadi, Oussama (University of Kasdi Marbah (Ouargla, Algèria.) LAGE Laboratory)
Kherfi, Mohammed Lamine (Université du Québec à Trois-Rivières. LAMIA Laboratory)
Khaldi, Belal (University of Kasdi Marbah (Ouargla, Algèria.) LAGE Laboratory)

Data:	2019
Resum:	In this paper, we propose a method for automatically recognizing different date varieties. The presence of outlier samples could significantly degrade the recognition outcomes. Therefore, we separately prune samples of each variety from outliers using the Pruning Local Distance-based Outlier Factor (PLDOF) method. Samples of the same variety could have several visual appearances because of the noticeable variation in terms of their visual characteristics. Thus, in order to take this intra-variation into account, we model each variety with a Gaussian Mixture Model (GMM), where each component within the GMM corresponds to one visual appearance. Expectation-Maximization (EM) algorithm was used for parameters estimation and Davies-Bouldin index was used to automatically and precisely estimate the number of components (i. e. , appearances). Compared to the related studies, the proposed method 1) is capable to recognize samples though the noticeable variation, in terms of maturity stage and hardness degree, within some varieties; 2) achieves a high recognition rate in spite of the presence of outlier samples; 3) is capable to distinguish between the highly confusing varieties; 4) is fully automatic, as it does not require neither physical measurements nor human assistance. For testing purposes, we introduce a new benchmark which includes the highest number of varieties (11) compared to the previous studies. Experiments show that our method has significantly outperformed several methods, where a high recognition rate of 97. 8% has been reached.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Date fruit ; Date recognition ; Gaussian mixture model ; Outlier detection
Publicat a:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 18 Núm. 1 (2019) , p. 52-75 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v18-n1-aiadi
DOI: 10.5565/rev/elcvia.1041

24 p, 1.9 MB

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