INDOOR-OUTDOOR IMAGE CLASSIFICATION USING DICHROMATIC REFLECTION MODEL AND HARALICK FEATURES

The problem of indoor-outdoor image classification using supervised learning is addressed in this paper. Conventional indoor-outdoor image classification methods, partition an image into predefined sub-blocks for feature extraction. However in this paper, we use a simple color segmentation stage to acquire meaningful regions from the image for feature extraction. The features that are used to describe an image are color correlated temperature, Haralick features, segment area and segment position. For the classification phase, an MLP was trained and tested using a dataset of 800 images. A classification accuracy of 94% compared with the result of other state of the art indoor-outdoor image classification methods showed the efficiency of the proposed method

Variational Reflectance Estimation from Multi-view Images

International audienceWe tackle the problem of reectance estimation from a set of multi-view images, assuming known geometry. The approach we put forward turns the input images into reectance maps, through a robust vari-ational method. The variational model comprises an image-driven delity term and a term which enforces consistency of the reectance estimates with respect to each view. If illumination is xed across the views, then reectance estimation remains under-constrained: a regularization term, which ensures piecewise-smoothness of the reectance, is thus used. Reectance is pa-rameterized in the image domain, rather than on the surface, which makes the numerical solution much easier , by resorting to an alternating majorization-minimization approach. Experiments on both synthetic and real-world datasets are carried out to validate the proposed strategy