Content-Based Image Retrieval Improved by Incorporating Semantic Annotation via Query Expansion

Automatic image annotation (AIA) is expected to be a promising way to improve the performance of content-based image retrieval (CBIR). However, current image annotation results are always incomplete and noisy, and far from practical usage. In this paper, we incorporate semantic annotations into CBIR via query expansion scheme to improve retrieval accuracy. In the proposed method, semantic annotations of test images are obtained using a visual nearest-neighbor-based annotation model. And both visual features and annotation keywords are used to represent images. The similarity between two images is determined by their visual similarity and semantic similarity. The method is evaluated on the well-known Pascal VOC 2007 dataset using standard performance evaluation metric. The experimental results indicate that the performance of CBIR can be improved by incorporating semantic annotation via query expansion.