Content-Based Image Retrieval Using Composite Feature Vectors with Edge Features Based on Color and Pixel Similarity

Content-based image retrieval involves searching for the desired image from an image database. It is realized using feature vectors obtained from the architectural image in question. Therefore, feature extraction is a crucial step. In this study, a novel feature vector representation method is proposed. In the proposed method, a composite feature vector is obtained by using color, edge, and gradient features. The most basic feature of the proposed method is that it uses the automatic pixel similarity approach for edge detection. The automatic pixel similarity approach offers a non-linear approach similar to the human visual system. Moreover, there is no need for any parameter or user intervention in edge detection. Additionally, the computational cost is much lower than those in many iterative non-linear edge detection approaches. In the study, experiments are carried out in the Corel-1K and Corel-10K databases, which are frequently used in image retrieval. The results of the proposed method are compared to those of 13 different methods. The superior performance of the proposed method is demonstrated. The high performance and low computational cost of the proposed method show that it can be easily implemented in many real-time image retrieval systems.