期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：9
DOI：10.14569/IJACSA.2016.070937
出版社：Science and Information Society (SAI)
摘要：Computers and artificial intelligence have penetrated in the food industry since last decade, for intellectual automatic processing and packaging in general, and in assisting for quality inspection of the food itself in particular. The food quality assessment task becomes more challenging when it is about harmless internal examination of the ingredient, and even more when its size is also minute. In this article, a method for automatic detection, extraction and classification of raw food item is presented using x-ray image data of pine nuts. Image processing techniques are employed in developing an efficient method for automatic detection and then extraction of individual ingredient, from the source x-ray image which comprises bunch of nuts in a single frame. For data representation, statistical texture analysis is carried out and attributes are calculated from each of the sample image on the global level as features. In addition co-occurrence matrices are computed from images with four different offsets, and hence more features are extracted by using them. To find fewer meaningful characteristics, all the calculated features are organized in several combinations and then tested. Seventy percent of image data is used for training and 15% each for cross-validation and test purposes. Binary classification is performed using two state-of-the-art non-linear classifiers: Artificial Neural Network (ANN) and Support Vector Machines (SVM). Performance is evaluated in terms of classification accuracy, specificity and sensitivity. ANN classifier showed 87.6% accuracy with correct recognition rate of healthy nuts and unhealthy nuts as 94% and 62% respectively. SVM classifier produced the similar accuracy achieving 86.3% specificity and 89.2% sensitivity rate. The results obtained are unique itself in terms of ingredient and promising relatively. It is also found that feature set size can be reduced up to 57% by compromising 3.5% accuracy, in combination with any of the tested classifiers.
