People have become increasingly dependent on the Web. However, it is difficult for visually impaired individuals to understand the content, especially if the web page contains images. The images can be more accessible by adding alt text, but it is known that alt text added through the current automation techniques are not necessarily helpful. Crowdsourcing is a promising approach for it, but adding alt text to all the images on the Web requires a tremendous amount of effort. In addition, too many alt texts of images also increase the difficulty of reading. Therefore, it is crucial to select important images for understanding web pages. This paper presents the results of our preliminary experiments to identify important images for understanding web pages. We adopted a crowdsourcing approach with two microtask designs. The results of our study demonstrated that (1) there are several types of images that are difficult to automatically assess the importance; thus, human-in-the-loop can be a promising approach to identify important ones in the types of images; and (2) there are microtask designs that can result in the similar results but different to each other in terms of other criteria. The results suggested that the identification of important images for understanding web pages is an interesting problem to address.