Inference Scene Labeling by Incorporating Object Detection with Explicit Shape Model

Conference Paper · November 2010with6 Reads
DOI: 10.1007/978-3-642-19318-7_30 · Source: DBLP
Conference: Computer Vision - ACCV 2010 - 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III


    In this paper, we incorporate shape detection into contextual scene labeling and make use of both shape, texture, and context
    information in a graphical representation. We propose a candidacy graph, whose vertices are two types of recognition candidates
    for either a superpixel or a window patch. The superpixel candidates are generated by a discriminative classifier with textural
    features as well as the window proposals by a learned deformable templates model in the bottom-up steps. The contextual and
    competitive interactions between graph vertices, in form of probabilistic connecting edges, are defined by two types of contextual
    metrics and the overlapping of their image domain, respectively. With this representation, a composite clustering sampling
    algorithm is proposed to fast search the optimal convergence globally using the Markov Chain Monte Carlo (MCMC). Our approach
    is applied on both lotus hill institute (LHI) and MSRC public datasets and achieves the state-of-art results.