SIR-DL: An architecture of semantic-based image retrieval using deep learning technique and RDF triple language

Abstract

The problem of finding and identifying semantics of images is applied in multimedia applications of many different fields such as hospital information system, geographic information system, digital library system, etc. In this paper, we propose the Semantic-Based Image Retrieval (SBIR) system based on the deep learning technique; this system is called as SIR-DL that generates visual semantics based on classifying image contents. Firstly, the color and spatial features of segmented images are extracted and these visual feature vectors are trained on the deep neural network to obtain visual words vectors. Then, we retrieve it on ontology to provide the identities and the semantics of similar images corresponds to a similarity measure. In order to carry out SIR-DL, the algorithms and diagram of this image retrieval system are proposed and after that we implement them on ImageCLEF@IAPR, which has 20,000 images. Based on experimental results, the effectiveness of our method is evaluated; these results are compared with some of the works recently published on the same image dataset. It shows that SIR-DL effectively solves the problem of SBIR and can be used to build multimedia system in many different fields.