Multiclass Fruit Classification of RGB-D Images using Color and Texture Feature 4th International Conference on Soft Computing, Intelligence System and Information Technology (ICSIIT) 2015
Ema Rachmawati, Iping Supriana, Masayu L. Khodra
[email protected] ,
[email protected] ,
[email protected]
Recognition problem Instance recognition
Category/class recognition
Grauman, K. and Leibe, B.2011. Visual Object Recognition. Synthesis Lectures Artif. Intell. Mach. Learn. 5, 1 181
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A sampling of kind image data in recognition system
Grauman, K. and Leibe, B.2011. Visual Object Recognition. Synthesis Lectures Artif. Intell. Mach. Learn. 5, 1 181 3
Background intra-class variations
inter-class variations
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Multi-view/multi-pose
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Most representative feature for multi class fruit recognition • Color, texture, and shape [Rocha dkk 2010] • Important aspects on building descriptor [Penatti dkk 2012]: • Scalability: the descriptor performance as the size of the collection increase • Diversity: the effect of heterogeneity of the collection to the descriptor effectiveness
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Color Features Extraction
Histogrambased
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Texture Features Extraction: Edge Histogram Descriptor
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Experiment
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Final Feature Vector simply combined the vector resulted from each extraction method
SCD: 32
EHD: 80
SCD & EHD
112 dimension
CLD: 33
CLD & EHD
EHD: 80
113 dimension
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Experiment : Dataset [8] 4129 images: 3861 images for training, 429 images for testing. 10-fold cross validation on Weka [21] & libsvm [22].
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Experiment: Results on 7 categories classification Texture: EHD
Color: SCD & CLD
Color & texture
ICSIIT, Bali, March 12nd, 2015
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Experiment: Results on 32 subcategories classification
Color: SCD & CLD
Texture: EHD
Color & texture
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CPU Time Training
Storage size
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Conclusion & Future Works • We presented a combined color and texture feature into a compact descriptor to classify multiclass fruit images with varying pose. • The result proves that our combined descriptor is giving high classification rate.
• Our future work will concentrate on improving classification accuracy with adding shape feature into existing color and texture descriptor on more complicated fruit images. • Further, by increasing the number of category and subcategory of fruit images, we hope the classification rate can be improved.
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Thank You ☺
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