Raisin Quality Classification Using Least Squares Support Vector Machine (LSSVM) Based on Combined Color and Texture Features

In this paper, an approach based on combined color and texture features to classify raisins is presented. Least squares support vector machine (LSSVM), linear discriminant analysis, and soft independent modeling of class analogy were used to construct classification models. A total of 480 images were captured from four grades of raisin samples by a Basler 601 fc IEEE1394 digital camera, 200 images were randomly selected to create calibration model (training set), and remaining images were used to verify the model (prediction set). Color features and texture features were obtained from two color spaces: red-green-blue and hue-saturation-intensity using histogram method and gray level co-occurrence matrix method, respectively. Our results indicate that the best performance with about 95% of average correct answer rate is achieved by LSSVM using combined color and texture features from HSI color space. This result is significantly higher than the performance of solely used color or texture features. The combined color and texture features coupled with a LSSVM classifier are a highly accurate way for raisin quality classification.