Cognitive Semantic Model for Visual Object Recognition in Image

Abstract

AbstractThe paper presents a hierarchical semantic model to perform object recognition in 2D images using cognitive neuroscience vision process. The proposed model contains two vital parts: Object-Features (OF) Conceptualization and Concept Recognition (CR). The model facilitates combination of multiple visual descriptors in the OF conceptualization and the CR process. The model comprises four major operation layers: Image Components Extraction (IE Layer), Visual Content Extraction (CE Layer), Visual Content Matching (CM Layer) and Object Recognition (OR Layer), arranged hierarchically from bottom (IE Layer) to top (OR Layer). The OR layer incorporates Multi-Level Thresholds technique, which defines various threshold values to control and finalize CR process. The experiments performed involved two types of visual descriptors: Color and Edge Directivity Descriptor (CEDD) and Fuzzy Color and Texture Histogram (FCTH). They were carried out using 9 set of images dataset. Different threshold values were tested to validate the feasibility and accuracy of the proposed model. Intensive empirical assessment has been performed and the results are promising.KeywordsObject RecognitionImage UnderstandingMultiple Feature DescriptorsHierarchical Semantic Model