Opposite Color Multiscale Local Binary Pattern Features for the Prediction of Bread Edibility

Bread is one of the profoundly consumed staple bakery foods by many people in the world. Quality is a remarkable concern as it is a consumable food product. It depends on the raw ingredients and baking process involved during the preparation. After the purchase of the bread, the quality and in turn the shelf life period of the bread may likely to get affected by the storage method. Hence, the edibility of the bread needs to be estimated. Most of the studies do estimate this using sensory attribute measurements like strange odor, crust color, taste, aroma, hard texture and mold formation. On contrary, this study newly attempts to examine the edibility effortlessly through images. A new variant of texture based Local Binary Pattern features is proposed for the prediction of edibility through analysis of hard texture and mold formation. As there is no benchmark bread sample dataset available for the study, a new dataset of 18,513 images is created. It is observed from the experimentation that the proposed Opposite Color Multiscale Local Binary Pattern features provide good estimation on majority voting with reduced number of features through feature transformation and selection. The accuracy obtained is 0.8493 which is comparable with other common variants of local binary pattern features. Multiple classifiers are evaluated during experimental analysis and ensemble approach outperforms well. As this is a contemporary problem addressed in the domain based on images of bread being first of its kind, it is likely to open up new challenges to be undertaken.