Title: Fruit and vegetable recognition by fusing colour and texture features of the image using machine learning
Authors: Shiv Ram Dubey; Anand Singh Jalal
Addresses: Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India ' Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India
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.
Keywords: multiclass SVM; support vector machines; machine learning; global colour histogram; GCH; colour coherence vector; CCV; fruit recognition; vegetable recognition; local binary pattern; local ternary pattern; LTP; completed LBP; CLBP; texture features; colour features; image fusion; fruits; vegetables; feature extraction; K-means clustering; image segmentation.
International Journal of Applied Pattern Recognition, 2015 Vol.2 No.2, pp.160 - 181
Received: 21 Feb 2014
Accepted: 27 May 2014
Published online: 22 May 2015 *