Focusing Attention on Visual Features that Matter

An indoor navigating agent needs to efficiently understand the geometric structure of its local environment in order to act. A common scene understanding approach is to generate a set of hypotheses about the geometric structure of the indoor environment and then test the hypotheses to select the one with the highest rank, from a single image [1, 4, 5, 6] or from a continuous stream of images (e.g. a video) [8, 9]. These methods simply detect features (e.g. lines [1, 4, 6], points [8, 9], and edges [6]) that are easily detectable for evaluating the hypotheses. In fact, some of the most informative features to discriminate the hypotheses may not be extracted if features are detected by fixed thresholds, since the informative regions may not have high image contrasts for features to be detected. This paper demonstrates that by focusing attention on features in the informative regions, we can evaluate the hypotheses more efficiently. The idea of focusing on informative regions of the image space is inspired by the idea of saliency detection [2, 3, 7]. While these works typically define saliency regions based on image and motion properties of the pixels in the images [2, 3] or based on human fixations [7], our informative regions are defined in terms of the agent’s own state of knowledge, the current set of hypotheses about the geometric structure of the indoor environment. Given a set M of hypotheses, we divide the image into regions based on the expected information gain that each feature provides, which we call informativeness (Figure 1). We define the informativeness I(p j,M)∈ [0,1] of point p j, measuring its discriminating power among the set M as,