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 the performance of other methods based on the ``bag of visual words'' approach.
local features random sampling global features dense sampling interest points detectors k-means algorithm co-occurrence matrix vector quantization colour moments keypoints local descriptors sparse sampling