Fruit and vegetable recognition by fusing colour and texture features of the image using machine learning

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.

[1]  Anand Singh Jalal,et al.  Species and variety detection of fruits and vegetables from images , 2013, Int. J. Appl. Pattern Recognit..

[2]  Nursuriati Jamil,et al.  Automated Grading of Palm Oil Fresh Fruit Bunches (FFB) Using Neuro-fuzzy Technique , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

[3]  Shiv Ram Dubey,et al.  Infected Fruit Part Detection using K-Means Clustering Segmentation Technique , 2013, Int. J. Interact. Multim. Artif. Intell..

[4]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Pietro Perona,et al.  Unsupervised learning of models for object recognition , 2000 .

[6]  Anand Singh Jalal,et al.  Automatic Fruit Disease Classification Using Images , 2014 .

[7]  Anderson Rocha,et al.  PR: More than Meets the Eye , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Gabriel Taubin,et al.  VeggieVision: a produce recognition system , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[9]  Ramin Zabih,et al.  Comparing images using color coherence vectors , 1997, MULTIMEDIA '96.

[10]  Jing-Yu Yang,et al.  Content-based image retrieval using color difference histogram , 2013, Pattern Recognit..

[11]  Seyed Hadi Mirisaee,et al.  A new method for fruits recognition system , 2009, 2009 International Conference on Electrical Engineering and Informatics.

[12]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[13]  Cordelia Schmid,et al.  Spatial Weighting for Bag-of-Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  Shiv Ram Dubey,et al.  Human Activity Recognition Using Gait Pattern , 2013, Int. J. Comput. Vis. Image Process..

[15]  Siwei Lyu,et al.  How realistic is photorealistic , 2005 .

[16]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[19]  J. P. Gupta,et al.  Semantic Image Retrieval by Combining Color, Texture and Shape Features , 2012, 2012 International Conference on Computing Sciences.

[20]  Michael Unser,et al.  Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Anand Singh Jalal,et al.  Adapted Approach for Fruit Disease Identification using Images , 2012, Int. J. Comput. Vis. Image Process..

[22]  Nong Sang,et al.  Detecting citrus fruits with shadow within tree canopy by a fusing method , 2012, 2012 5th International Congress on Image and Signal Processing.

[23]  Riad I. Hammoud,et al.  Distinguishing paintings from photographs , 2005, Comput. Vis. Image Underst..

[24]  Trevor Darrell,et al.  Efficient image matching with distributions of local invariant features , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[25]  Anand Singh Jalal,et al.  Robust Approach for Fruit and Vegetable Classification , 2012 .

[26]  Jiebo Luo,et al.  A computationally efficient approach to indoor/outdoor scene classification , 2002, Object recognition supported by user interaction for service robots.

[27]  A. S. Jalal,et al.  Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns , 2012, 2012 Third International Conference on Computer and Communication Technology.

[28]  Anand Singh Jalal,et al.  Fruit disease recognition using improved sum and difference histogram from images , 2014, Int. J. Appl. Pattern Recognit..

[29]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[30]  Frédéric Jurie,et al.  Creating efficient codebooks for visual recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[31]  Gunther Heidemann,et al.  Unsupervised image categorization , 2005, Image Vis. Comput..

[32]  Bernard Gosselin,et al.  Artificial neural network-based segmentation and apple grading by machine vision , 2005, IEEE International Conference on Image Processing 2005.

[33]  R. N. Shebiah,et al.  Fruit Recognition using Color and Texture Features , 2010 .

[34]  Shitala Prasad,et al.  Sports Video Summarization using Priority Curve Algorithm , 2010 .

[35]  Jacques Wainer,et al.  Automatic fruit and vegetable classification from images , 2010 .

[36]  Jitendra Malik,et al.  Shape matching and object recognition using low distortion correspondences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[37]  Mario A. Nascimento,et al.  A compact and efficient image retrieval approach based on border/interior pixel classification , 2002, CIKM '02.

[38]  Xingyuan Wang,et al.  A novel method for image retrieval based on structure elements' descriptor , 2013, J. Vis. Commun. Image Represent..