An intelligent system to estimate and classify the agricultural and food products using coloring local features

 
 
 
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  • Abstract


    Color is commonly perceived as an indispensable quality in describing edible nuts, fruits, vegetables and food grains. State-of-the art local feature-based representations are mostly based on shape description, and ignore color information. The measured color values vary signifi-cantly due to large amount of variations, which in turn hamper the description of color. The aim of this paper is to extend the description of local features of images of agricultural and food products with color information. To accomplish a wide applicability of the color descriptor, it should be robust to the photometric changes that are commonly encountered in the images of agricultural and food products and also the varying image quality ranging from high quality images to snap-shot photo quality and compressed images. Based on these requirements we derive a set of color descriptors. The set of proposed descriptors are compared by extensive testing on agricultural and food products images, namely, matching, retrieval and classification and on wide variety of image qualities. The results show that the color descriptors remain reli-able under photometric and geometrical changes, and also for poor image quality. For all the experiments carried out, it is observed that a combination of color and shape based–approach outperforms a pure shape-based approach.

     

     


  • Keywords


    Agricultural and Food Products Images; Classification; Matching; Local Features.

  • References


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Article ID: 14728
 
DOI: 10.14419/ijet.v7i4.14728




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