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Product Image Categorization Image categorization usually refers to "the labeling of images into one of a number of predefined categories", which has been an important research topic in past decades. Different taxonomy definitions lead to different problems with very different solutions. For example, a few previous approaches categorize images by their generation method, such as photographs and graphics. There are also some work defining image category as a high-level concept hierarchy, such as indoor/outdoor or cityscape/landscape. In this project, we focus on a particular type of taxonomy: product category. In other words, an image is categorized by the product type of its main object, such as camera or guitar. We realize that in general, categorizing objects is a very difficult computer vision task. In order to build a practical system, two requirements are introduced for our data set:
Data Set - PI 100 One million candidate images in 400 categories were collected from MSN shopping web site (http://shopping.msn.com/). Two graduate students were invited to select 100 categories with 120 images in each category from these images. When selecting the database images, we asked them to follow the two requirements and remove all duplicate images. We randomly split the 120 images of each category into two sets: 100 as the database set, and 20 as the query set. The following figure shows one sample image from each category in our database.  File Download
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