Evaluation on the Impact of Image Quality on Image Retrieval

In recent years, content-based image retrieval (CBIR) using local invariant features has been a hot research topic. In CBIR, the retrieval performance, such as accuracy and efficiency, is significantly affected by the quality of the query image. Generally, the image quality is determined by many factors, such as image resolution, noise addition, rotation, JPEG compression, selected local features, etc. In this paper, we make a comprehensive study on those factors to investigate their impact on image search accuracy. We build the baseline system with the classic Bag-of-Visual-Words model and the inverted index structure. Two public released datasets, i.e., UKBench and Oxford Building, are selected as ground truth dataset. Based on the extensive experimental study, some conclusions are drawn from the evaluation results. In UKBench dataset, performance keeps stable if the image size is controlled in a certain range of 384×288 to 576×432. Also, the JPEG conpreession ratio can be reduced to as low as 8% of base one but has little impact on retrieval performance. What is more, Image performance achieves 90% of best result though its PSNR value is 32 when we test in Oxford Building dataset.