Fruit and vegetable recognition by fusing colour and texture features of the image using machine learning Online publication date: Fri, 22-May-2015
by Shiv Ram Dubey; Anand Singh Jalal
International Journal of Applied Pattern Recognition (IJAPR), Vol. 2, No. 2, 2015
Abstract: Efficient and accurate recognition of fruits and vegetables from the images is one of the major challenges for computers. In this paper, we introduce a framework for the fruit and vegetable recognition problem which takes the images of fruits and vegetables as input and returns its species and variety as output. The input image contains fruit or vegetable of single variety in arbitrary position and in any number. The whole process consists of three steps: 1) background subtraction; 2) feature extraction; 3) training and classification. K-means clustering-based image segmentation is used for background subtraction. We extracted different state-of-art colour and texture features and combined them to achieve more efficient and discriminative feature description. Multi-class support vector machine is used for the training and classification purpose. The experimental results show that the proposed combination scheme of colour and texture features supports accurate fruit and vegetable recognition and performs better than stand-alone colour and texture features.
Online publication date: Fri, 22-May-2015
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