Common-Frame Model for Object Recognition

A generative probabilistic model for objects in images is presented. An object consists of a constellation of features. Feature appearance and pose are modeled probabilistically. Scene images are generated by drawing a set of objects from a given database, with random clutter sprinkled on the remaining image surface. Occlusion is allowed. We study the case where features from the same object share a common reference frame. Moreover, parameters for shape and appearance densities are shared across features. This is to be contrasted with previous work on probabilistic `constellation' models where features depend on each other, and each feature and model have different pose and appearance statistics [1, 2]. These two differences allow us to build models containing hundreds of features, as well as to train each model from a single example. Our model may also be thought of as a probabilistic revisitation of Lowe's model [3, 4]. We propose an efficient entropy-minimization inferenc...

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Pierre Moreels, Pietro Perona
• Keywords: Generative Probabilistic Model, Image, NIPS 2004, NIPS 2007, Probabilistic