Evaluation of histogram based interest point detector in web image classification and search

Local image feature has received increasing attention in various applications, such as web image classification and search. The process of local feature extraction consists of two main steps: interest point detection and local feature description. A wealth of interest point detectors have been proposed in last decades. Most of them measure pixel-wise differences in image intensity or color. Recently, a new type of interest point detector has been developed, which incorporates histogram-based representation into the process of interest point detection. In this paper, we evaluate this histogram-based interest point detector in the context of web image classification and search, as well as compare it against typical pixel-based detectors and heuristic grid-based detector. The evaluation is performed on two web image datasets: NUS-WIDE-OBJECT and MIRFLICKR-25000 datasets. The experimental results demonstrate that the histogram-based interest point detector outperforms the pixel-based and grid-based detectors in both web image classification and search tasks.