Authors: Shiv Ram Dubey; Anand Singh Jalal
Addresses: GLA University, 17 km Stone, NH-2, Mathura-Delhi Highway, P.O. Chaumuhan, Mathura-281 406 (U.P.), India ' GLA University, 17 km Stone, NH-2, Mathura-Delhi Highway, P.O. Chaumuhan, Mathura-281 406 (U.P.), India
Abstract: Efficient detection of 'species and variety' of fruits and vegetables from the images is one of the major challenges for the computers. In this paper, we introduce a framework for the fruit and vegetable classification problem which takes the images of fruits and vegetables as input and returns it is species and variety. The input image contains fruit or vegetable of single variety in arbitrary position and in any number. This paper also introduces a texture feature based on sum and difference of intensity values of the neighbouring pixels of the colour images. The experimental results show that the proposed texture feature supports accurate fruit and vegetable recognition and performs better than other state-of-the-art colour and texture features. The classification accuracy for the proposed ISADH texture feature is achieved up to 99%.
Keywords: multiclass SVM; support vector machines; sum and difference histogram; global colour histogram; GCH; colour coherence vector; CCV; species detection; variety detection; fruit and vegetables; image recognition; texture features; colour images; fruit recognition; vegetable recognition.
International Journal of Applied Pattern Recognition, 2013 Vol.1 No.1, pp.108 - 126
Received: 21 Sep 2012
Accepted: 24 Oct 2012
Published online: 31 Jul 2014 *