10.1.1.118.2575

10.1.1.118.2575 - 530 IEEE TRANSACTIONS ON PATTERN ANALYSIS...

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Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues David R. Martin, Member , IEEE , Charless C. Fowlkes, and Jitendra Malik, Member , Abstract —The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images. Index Terms —Texture, supervised learning, cue combination, natural images, ground truth segmentation data set, boundary detection, boundary localization. æ 1I NTRODUCTION C ONSIDER the images and human-marked boundaries shown in Fig. 1. How might we find these boundaries automatically? We distinguish the problem of boundary detection from what is classically referred to as edge detection. A boundary is a contour in the image plane that represents a change in pixel ownership from one object or surface to another. In contrast, an edge is most often defined as an abrupt change in some low-level image feature such as brightness or color. Edge detection is thus one low-level technique that is commonly applied toward the goal of boundary detection. Another approach would be to recognize objects in the scene and use that high-level information to infer the boundary locations. In this paper, we focus on what information is available in a local image patch like those shown in the first column of Fig. 2. Though these patches lack global context, it is clear to a human observer which contain boundaries and which do not. Our goal is to use features extracted from such an image patch to estimate the posterior probability of a boundary passing through the center point. A boundary model based on such local information is likely to be integral to any perceptual organization algorithm that operates on natural images, whether based on grouping pixels into regions [1], [2] or grouping edge fragments into contours [3], [4]. This paper is intentionally agnostic about how a local boundary model might be used in a system for performing a high-level visual task such as recognition. The most common approach to local boundary detection is to look for discontinuities in image brightness. For example, the Canny detector [5] models boundaries as brightness step edges. The brightness profiles in the second column of Fig. 2 show that this is an inadequate model for boundaries in natural images where texture is a ubiquitous phenomenon. The Canny detector fires wildly inside
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10.1.1.118.2575 - 530 IEEE TRANSACTIONS ON PATTERN ANALYSIS...

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