This paper proposes a novel method of discovering a set of image contents sharing a specific context (attributes or implicit meaning) with the help of image collections obtained from social curation platforms. Socially curated contents are promising to analyze various kinds of multimedia information, since they are manually filtered and organized based on specific individual preferences, interests or perspectives. Our proposed method fully exploits the process of social curation: (1) How image contents are manually grouped together by users, and (2) how image contents are distributed in the platform. Our method reveals the fact that image contents with a specific context are naturally grouped together and every image content includes really various contexts that cannot necessarily be verbalized by texts.% A preliminary experiment with a small collection of a million of images yields a promising result.

This paper proposes a stereo method robust against occlusions that can reconstruct 3D building facades from images captured from a vehicle. When using ground-based views of facades, the occlusion problem caused by roadside trees and telegraph poles in urban areas is the one of the most difficult barriers. In this study, we show the stereo matching based on parallel images minimize the occlusion effect. To acquire the parallel images, we use two line scan cameras linked a rotary encoder mounted on a vehicle. Experimental results using outdoor scenes confirm the effectiveness of this method.