Scene classification is a major open challenge in machine vision. Most solutions proposed so far such as those based on color histograms and local texture statistics cannot capture a scene's global configuration, which is critical in perceptual judgments of scene similarity. We present a novel approach, "configural recognition", for encoding scene class structure. The approach's main feature is its use of qualitative spatial and photometric relationships within and across regions in low resolution images. The emphasis on qualitative measures leads to enhanced generalization abilities and the use of low-resolution images renders the scheme computationally efficient. We present results on a large database of natural scenes. We also describe how qualitative scene concepts may be learned from examples.
We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systemat...
The histogram of image intensities is used extensively for recognition and for retrieval of images and video from visual databases. A single image histogram, however, suffers fr...
The recognition of shapes in images using Pairwise Geometric Histograms has previously been confined to fixed scale shape. Although the geometric representation used in this alg...
A new algorithm is presented which approximates the perceived visual similarity between images. The images are initially trans-formed into a feature space which captures visual ...
We propose a scheme for indoor place identification based on the recognition of global scene views. Scene views are encoded using a holistic representation that provides low-res...
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