Learning primitive and scene semantics of images for classification and retrieval

Abstract

We present a learning-based semantics approach for classifying and retrieving images. Our approach defines semantics at two levels: (1) primitive semantics at the patch level, extracted automatically from pixel characteristics of patches with supervised learning; and (2) scene semantics at the image level, recognized from the association of primitive semantics of the patches in a self-organizing manner. Images are classified and retrieved according to the similarity in scene semantics. Our experiments so far have yielded highly accurate scene classification results and very promising retrieval performance on a set of diverse natural scene images.