Learn More
We present a latent hierarchical structural learning method for object detection. An object is represented by a mixture of hierarchical tree models where the nodes represent object parts. The nodes can move spatially to allow both local and global shape deformations. The models can be trained discriminatively using latent structural SVM learning, where the(More)
A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1, 2] is a sequential forward search procedure using the greedy selection strategy. The premise oÿered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new(More)
A general classification framework, called boosting chain, is proposed for learning boosting cascade. In this framework, a “chain” structure is introduced to integrate historical knowledge into successive boosting learning. Moreover, a linear optimization scheme is proposed to address the problems of redundancy in boosting learning and threshold adjusting(More)
In this paper, we address the tasks of detecting, segmenting, parsing, and matching deformable objects. We use a novel probabilistic object model that we call a hierarchical deformable template (HDT). The HDT represents the object by state variables defined over a hierarchy (with typically five levels). The hierarchy is built recursively by composing(More)
We describe a new method for unsupervised structure learning of a hierarchical compositional model (HCM) for deformable objects. The learning is unsupervised in the sense that we are given a training dataset of images containing the object in cluttered backgrounds but we do not know the position or boundary of the object. The structure learning is performed(More)
We present a novel structure learning method, Max Margin AND/OR graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human body and its parts by an AND/OR graph, which is a multi-level mixture of Markov random fields (MRFs). Max-margin learning, which is a generalization of the training algorithm for(More)
We introduce a probabilistic grammar-Markov model (PGMM) which couples probabilistic context free grammars and Markov random fields. These PGMMs are generative models defined over attributed features and are used to detect and classify objects in natural images. PGMMs are designed so that they can perform rapid inference, parameter learning, and the more(More)
In this paper we formulate a novel AND/OR graph representation capable of describing the different configurations of deformable articulated objects such as horses. The representation makes use of the summarization principle so that lower level nodes in the graph only pass on summary statistics to the higher level nodes. The probability distributions are(More)
We propose Recursive Compositional Models (RCMs) for simultaneous multi-view multi-object detection and parsing (e.g. view estimation and determining the positions of the object subparts). We represent the set of objects by a family of RCMs where each RCM is a probability distribution defined over a hierarchical graph which corresponds to a specific object(More)