Image search engines currently available in the market are in many ways insufficient in providing quick, well-organized results. When a key word is entered, usually a long list of image results that covers a whole range of themes appears, often because of simplicity or ambiguity in the key word entered. Raking through these results could be extremely tedious, like searching for the right outfit in a chaotic closet. However, it has been found that people are more likely to flip through the given results than alter their key word, possibly due to uncertainty as to what exactly to enter in the search box.

In response to this problem, two researchers at Microsoft Research Asia, Feng Jing and Shuo Wang, have introduced a new research project called "IGroup" (Image Group), which, taking on the characteristics of its creators, is a unique innovation, stemming from a breakthrough out of traditional ideas. Many of the key technology involved have already gone for patent applications.

IGroup can be built upon any basic image search engine. It splits the search results page into two sections, one section displaying topic groups related to the key word plus thumbnails, and another section displaying the full image search results. This is similar to a closet that can automatically organize different clothes types into different sections of itself. For example, if you search for "Tiger," not only will the topic groups display all the different tiger species, but will also have listed anything else related to the word, such as golf player Tiger Woods. Users can first choose from the list of topics or thumbnails offered to narrow the search, and then look at the images resulting from the more specific search in the other section.

In actuality, the idea of grouping image results has been around for a while. Traditionally, images have been grouped based on identification of similar features, but this can prove to be slow and lacking in effectiveness. IGroup, on the other hand, uses a clever method with multiple search processes to obtain a wide range of results in a short amount of time.

IGroup still faces certain problems, such as how to group topics together accurately and how to eliminate irrelevant images. However, users can be assured that the future for image search is bright, with smarter and more convenient search options to be introduced.