6533b821fe1ef96bd127c0be

RESEARCH PRODUCT

Benchmarking Saliency Detection Methods on Multimodal Image Data

Alamin MansouriGaëtan Le GoïcDriss MammassHanan AnzidAissam Bekkari

subject

Modality (human–computer interaction)Similarity (geometry)Computer sciencebusiness.industry05 social sciencesMultispectral imageComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognition02 engineering and technologyBenchmarking050105 experimental psychologyMultimodal imageMetric (mathematics)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processing0501 psychology and cognitive sciencesSaliency mapArtificial intelligencebusiness

description

Saliency detecmage processing. Most of the work is adapted to the specific application and available dataset. The present work is about a comparative analysis of saliency detection for multimodal images dataset. There were many researches on the detection of saliency on several types of images, such as multispectral, natural, 3D and so on. This work presents a first focused study on saliency detection on multimodal images. Our database was extracted from acquisitions on cultural heritage wall paintings that contain four modalities UV, IR, Visible and fluorescence. In this paper, the analysis has been performed for many methods on saliency detection. We evaluate the performance of each method using NSS similarity metric. The results show that the best methods are [16] for visible modality, and [20] for UV, IR and fluorescence modalities.

yearjournalcountryeditionlanguage
2018-01-01
https://doi.org/10.1007/978-3-319-94211-7_2