Handwritten feature descriptor methods applied to fruit classification

Several works have presented distinct ways to compute feature descriptor from different applications and domains. A main issue in Computer Vision systems is how to choose the best descriptor for specific domains. Usually, Computer Vision experts try several combination of descriptor until reach a good result of classification, clustering or retrieving – for instance, the best descriptor is that capable of discriminating the dataset images and reach high correct classification rates. In this paper, we used feature descriptors commonly applied in handwritten images to improve the image classification from fruit datasets. We present distinct combinations of Zoning and Character-Edge Distance methods to generate feature descriptor from fruits. The combination of these two descriptor with Discrete Fourier Transform led us to a new approach for acquire features from fruit images. In the experiments, the new approaches are compared with the main descriptors presented in the literature and our best approach of feature descriptors reaches a correct classification rate of 97.5%. Additionally, we also show how to perform a detailed inspection in feature spaces through an image visualization technique based on a similarity trees known as Neigbor Joining (NJ).

[1]  Danilo Medeiros Eler,et al.  Performance Indicators Analysis in Software Processes Using Semi-supervised Learning with Information Visualization , 2016 .

[2]  Ranjan Parekh,et al.  Intra-class Recognition of Fruits using Color and Texture Features with Neural Classifiers , 2016 .

[3]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Ke Huang,et al.  Rotation Invariant Texture Classification with Ridgelet Transform and Fourier Transform , 2006, 2006 International Conference on Image Processing.

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

[6]  Luiz Eduardo Soares de Oliveira,et al.  Metaclasses and Zoning Mechanism Applied to Handwriting Recognition , 2008, J. Univers. Comput. Sci..

[7]  Rosane Minghim,et al.  Point Placement by Phylogenetic Trees and its Application to Visual Analysis of Document Collections , 2007, 2007 IEEE Symposium on Visual Analytics Science and Technology.

[8]  Saswati Naskar,et al.  A Novel Fruit Recognition Technique using Multiple Features and Artificial Neural Network , 2015 .

[9]  Mário Augusto Pazoti,et al.  ALGORITMO PARA O RECONHECIMENTO DE CARACTERES MANUSCRITOS , 2013 .

[10]  W. S. Lee,et al.  Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions , 2014 .

[11]  Aboul Ella Hassenian,et al.  Automatic Fruit Image Recognition System Based on Shape and Color Features , 2014, AMLTA.

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

[13]  Danilo Medeiros Eler,et al.  Visual analysis of image collections , 2009, The Visual Computer.