Product search framework with categorization and identification
When people want to find some products in the Internet. They use query words. In this paper, we propose a product search framework with images instead of words. This is helpful when user doesnpsilat know about a product or want to find similar products. The framework is composed of three parts: (1) classify a category of product (2) find a corresponding product (3) retrieve similar products by its shape or color. We use local features as a visual information and adopt visual words based method in the categorization and identification. The representation of image is a histogram which is made from a set of local features from an image and visual words from image set. As a local feature, we adopt SURF. We use grid sampling method in the categorization part and original Fast-Hessian detector in the identification part. In the similar product search, we use a binary resized image and color information. We validate our product search framework with KAIST-104 DB and our product DB.