Novel local features with hybrid sampling technique for image retrieval

In image retrieval, most existing approaches that incorporate local features produce high dimensional vectors, which lead to a high computational and data storage cost. Moreover, when it comes to the retrieval of generic real-life images, randomly generated patches are often more discriminant than the ones produced by corner/blob detectors. In order to tackle these problems, we propose a novel method incorporating local features with a hybrid sampling (a combination of detector-based and random sampling). We take three large data collections for the evaluation: MIRFlickr, ImageCLEF, and a collection from British National Geological Survey. The overall performance of the proposed approach is better than the performance of global features and comparable with the current state-of-the-art methods in content-based image retrieval. One of the advantages of our method when compared with others is its easy implementation and low computational cost. Another is that hybrid sampling can improve ...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Leszek Kaliciak, Dawei Song, Nirmalie Wiratunga, Jeff Pan
• Keywords: CIKM 2010, Content-based Image Retrieval, Image Retrieval, Information Technology, Local Features