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.