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The constellation model is a probabilistic, generative model for category-level object recognition in computer vision. Like other part-based models, the constellation model attempts to represent an object class by a set of ''N'' parts under mutual geometric constraints. Because it considers the geometric relationship between different parts, the constellation model differs significantly from appearance-only, or "bag-of-words" representation models, which explicitly disregard the location of image features. The problem of defining a generative model for object recognition is difficult. The task becomes significantly complicated by factors such as background clutter, occlusion, and variations in viewpoint, illumination, and scale. Ideally, we would like the particular representation we choose to be robust to as many of these factors as possible. In category-level recognition, the problem is even more challenging because of the fundamental problem of intra-class variation. Even if two objects belong to the same visual category, their appearances may be significantly different. However, for structured objects such as cars, bicycles, and people, separate instances of objects from the same category are subject to similar geometric constraints. For this reason, particular parts of an object such as the headlights or tires of a car still have consistent appearances and relative positions. The Constellation Model takes advantage of this fact by explicitly modeling the relative location, relative scale, and appearance of these parts for a particular object category. Model parameters are estimated using an unsupervised learning algorithm, meaning that the visual concept of an object class can be extracted from an unlabeled set of training images, even if that set contains "junk" images or instances of objects from multiple categories. It can also account for the absence of model parts due to appearance variability, occlusion, clutter, or detector error. == History == The idea for a "parts and structure" model was originally introduced by Fischler and Elschlager in 1973.〔(M. Fischler and R. Elschlager. ''The Representation and Matching of Pictoral Structures.'' (1973) )〕 This model has since been built upon and extended in many directions. The Constellation Model, as introduced by Dr. Perona and his colleagues, was a probabilistic adaptation of this approach. In the late '90s, Burl et al.〔 (M. Burl, T. Leung, and P. Perona. ''Face Localization via Shape Statistics.'' (1995) )〕〔(T. Leung, M. Burl, and P. Perona. ''Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching.'' (1995) )〕〔(M. Burl and P. Perona. ''Recognition of Planar Object Classes'' (1996) )〕〔(M. Burl, M. Weber, and P. Perona. ''A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry'' (1998) )〕 revisited the Fischler and Elschlager model for the purpose of face recognition. In their work, Burl et al. used manual selection of constellation parts in training images to construct a statistical model for a set of detectors and the relative locations at which they should be applied. In 2000, Weber et al. 〔(M. Weber. ''Unsupervised Learning of Models for Object Recognition.'' PhD Thesis. (2000) )〕〔(M. Weber, W. Einhaeuser, M. Welling and P. Perona. ''Viewpoint-Invariant Learning and Detection of Human Heads.'' (2000) )〕〔(M. Weber, M. Welling, and P. Perona. ''Towards Automatic Discovery of Object Categories.'' (2000) )〕〔(M. Weber, M. Welling and P. Perona. ''Unsupervised Learning of Models for Recognition.'' (2000) )〕 made the significant step of training the model using a more unsupervised learning process, which precluded the necessity for tedious hand-labeling of parts. Their algorithm was particularly remarkable because it performed well even on cluttered and occluded image data. Fergus et al.〔(R. Fergus, P. Perona, and A. Zisserman. ''Object Class Recognition by Unsupervised Scale-Invariant Learning.'' (2003) )〕〔(R. Fergus. ''Visual Object Category Recognition.'' PhD Thesis. (2005) )〕 then improved upon this model by making the learning step fully unsupervised, having both shape and appearance learned simultaneously, and accounting explicitly for the relative scale of parts. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「constellation model」の詳細全文を読む スポンサード リンク
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