Paper Info
Title
Fruit And Vegetable Recognition By Fusing Colour And Texture Features Of The Image Using Machine Learning
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.
Year
DOI
Venue
2015
10.1504/IJAPR.2015.069538
INTERNATIONAL JOURNAL OF APPLIED PATTERN RECOGNITION
Keywords
DocType
Volume
multiclass SVM, machine learning, global colour histogram, GCH, colour coherence vector, CCV, fruit recognition, local binary pattern, LBP, local ternary pattern, LTP, completed local binary pattern, CLBP, texture
Journal
2
Issue
ISSN
Citations 
2
2049-887X
0
PageRank 
References 
Authors
0.34
20
2
Name
Order
Citations
PageRank
Shiv Ram Dubey132524.44
Anand Singh Jalal213828.45