Abstract

In this paper we propose a new and effective scheme for applying frequent itemset mining to image classification tasks. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During the construction of the FLHs, we pay special attention to keep all the local histogram information during the mining process and to select the most relevant reduced set of FLH patterns for classification. The careful choice of the visual primitives and some proposed extensions to exploit other visual cues such as colour or global spatial information allow us to build powerful bag-of-FLH-based image representations. We show that these bag-of-FLHs are more discriminative than traditional bag-of- words and yield state-of-the art results on various image classification benchmarks.