Tagging products using image classification

Abstract

Associating labels with online products can be a labor-intensive task. We study the extent to which a standard "bag of visual words" image classifier can be used to tag products with useful information, such as whether a sneaker has laces or velcro straps. Using Scale Invariant Feature Transform (SIFT) image descriptors at random keypoints, a hierarchical visual vocabulary, and a variant of nearest-neighbor classification, we achieve accuracies between 66% and 98% on 2- and 3-class classification tasks using several dozen training examples. We also increase accuracy by combining information from multiple views of the same product.