Adaptive Image Representation Using Information Gain and Saliency: Application to Cultural Heritage Datasets

Abstract

Recently, the advent of deep neural networks showed great performances for supervised image analysis tasks. However, image expert datasets with little information or prior knowledge still need indexing tools that best represent the expert wishes. Our work fits in this very specific application context where only few expert users may appropriately label the images. Thus, in this paper, we consider small expert collections with no associated relevant label set, nor structured knowledge. In this context, we propose an automatic and adaptive framework based on the well-known bags of visual words and phrases models that select relevant visual descriptors for each keypoint to construct a more discriminating image representation. In this framework, we mix an information gain model and visual saliency information to enhance the image representation. Experiment results show the adaptiveness and the performance of our unsupervised framework on well-known “generic” datasets and also on a cultural heritage expert dataset.