PLSA driven image annotation, classification, and tourism recommendation

Abstract

A burst of interest in image annotation and recommendation has been witnessed. Despite the huge effort made by the scientific community in the aforementioned research areas, accuracy or efficiency still remain open problems. Here, efficient methods for image annotation, visual image content classification as well as touristic place of interest (POI) recommendation are developed within the same framework. In particular, semantic image annotation and touristic POI recommendation harness the geo-information associated to images. Both semantic image annotation and visual image content classification resort to Probabilistic Latent Semantic Analysis (PLSA). Several tourist destinations, strongly related to the query image, are recommended, using hypergraph ranking. Experimental results were conducted on a large image dataset of Greek sites, demonstrating the potential of the proposed methods. Semantic image annotation by means of PLSA has achieved an average precision of 90% at 10% recall. The average accuracy of content-based image classification is 80%. An average precision of 90% is measured at 1% recall for tourism recommendation.