A New Retrieval System Based on Low Dynamic Range Expansion and SIFT Descriptor

Abstract

When comparing the intelligibility of Low Dynamic Range (LDR) content with that of the human visual system (HSV), we find that the former is quite limited. This is straightforward, as LDR technologies could only handle 8 or 12-bit per color channel. This being said, LDR images are still used in a wide range of multimedia applications. This paper presents a solution for efficiently indexing LDR content by introducing a novel descriptor based on LDR content expansion. To increase the richness of features that are strongly dependent on the illumination of the scene, the LDR image is converted to High Dynamic Range (HDR) one using reverse Tone Mapping Operator (rTMO). The result HDR image is in turn tone mapped and the relevant features are determined according the Scale Invariant Feature Transform (SIFT) descriptor. After that, the obtained features are gathered into a vector using Bag-of-Visual-Word (BoVW) strategy. A set of routine benchmarking experiments utilizing the Wang and Pascal Voc databases indicates that our system performs well for image retrieval. These experiments also demonstrate that features extracted from reverse tone mapped and tone mapped image are more descriptive than those extracted from LDR and HDR contents.