Integrating semantic features in fruit recognition based on perceptual color and semantic template

Abstract

Abstract Many fruit recognition works have applied statistical approaches to make an exact correlation between low-level visual feature information and high-level semantic concepts given by predefined text caption or keywords. Two common fruit recognition models include bag-of-features (BoF) and convolutional neural network (ConvNet), which achieve high-performance results. In most cases, the overfitting problem is unavoidable. This problem makes it difficult to generalize new instances with only a slightly different appearance, although belonging to the same category. This article proposes a new fruit recognition model by associating an object s low-level features in an image with a high-level concept. We define a perceptual color for each fruit species to construct a relationship between fruit color and semantic color name. Furthermore, we develop our model by integrating the perceptual color and semantic template concept to solve the overfitting problem. The semantic template concept as a mapping between the high-level concept and the low-level visual feature is adopted in this model. The experiment was conducted on three different fruit image datasets, with one dataset as train data and the two others as test data. The experimental results demonstrate that the proposed model, called perceptual color on semantic template (PCoST), is significantly better than the BoF and ConvNet models in reducing the overfitting problem.