13332239
A Hierarchical Markov Random Field Model for Figure-Ground Segregation
To segregate overlapping objects into depth layers requires the integration of local occlusion cues distributed over the entire image into a global percept. We propose to model this process using hierarchical Markov random field (HMRF), and suggest a broader view that clique potentials in MRF models can be used to encode any local decision rules. A topology-dependent multiscale hierarchy is used to introduce long range interaction. The operations within each level are identical across the hierarchy. The clique parameters that encode the relative importance of these decision rules are estimated using an optimization technique called learning from rehearsals based on 2-object training samples. We find that this model generalizes successfully to 5-object test images, and that depth segregation can be completed within two traversals across the hierarchy. This computational framework therefore provides an interesting platform for us to investigate the interaction of local decision rules and global representations, as well as to reason about the rationales underlying some of recent psychological and neurophysiological findings related to figure-ground segregation.
122735646
Segmentation using multiscale cues
Edges at multiple scales provide complementary grouping cues for image segmentation. These cues are reliable within different ranges. The larger the scale of an edge, the longer range the grouping cues it designates, and the greater impact it has on the final segmentation. A good segmentation respects grouping cues at each scale. These intuitions are formulated in a graph-theoretic framework where multiscale edges define pairwise pixel affinity at multiple grids, each captured in one graph. A novel criterion called average cuts of normalized affinity is proposed to evaluate a simultaneous segmentation through all these graphs. Its near-global optima can be solved efficiently. With a sparse yet complete characterization of pairwise pixel affinity, this graph-cuts approach leads to a hierarchy of coarse to fine segmentations that naturally take care of textured regions and weak contours.
17770576
Practical rules for reduction on the number of states of a state diagram
Many object-oriented modeling methods adopt state-based formalisms to specify the behavior of objects. State-based formalisms, pioneered by David Harel (1987), are a powerful and yet intuitive tool in modeling the dynamics of objects. However, when a real world object has a huge number (or an infinite number) of states, we usually have to map them into a relatively small number of states in practice. This mapping has been done intuitively and heuristically by software designers. There has not been a set of general and systematic rules for this mapping. We focus on exploring a set of such rules with which a system of a large number (or an infinite number) of states can be transformed systematically into an equivalent finite-state system with a smaller number of states. Examples are given and the suggested rules are explained.
9400780
Bayesian image classification using Markov random fields
In this paper, we present three optimisation techniques, Deterministic Pseudo-Annealing (DPA), Game Strategy Approach (GSA), and Modified Metropolis Dynamics (MMD), in order to carry out image classification using a Markov random field model. For the first approach (DPA), the a posteriori probability of a tentative labelling is generalised to a continuous labelling. The merit function thus defined has the same maxima under constraints yielding probability vectors. Changing these constraints convexifies the merit function. The algorithm solves this unambiguous maximisation problem, and then tracks down the solution while the original constraints are restored yielding a good, even if suboptimal, solution to the original labelling assignment problem. In the second method (GSA), the maximisation problem of the a posteriori probability of the labelling is solved by an optimisation algorithm based on game theory. A non-cooperative n-person game with pure strategies is designed such that the set of Nash equilibrium points of the game is identical to the set of local maxima of the a posteriori probability of the labelling. The algorithm converges to a Nash equilibrium. The third method (MMD) is a modified version of the Metropolis algorithm: at each iteration the new state is chosen randomly, but the decision to accept it is purely deterministic. This is also a suboptimal technique but it is much faster than stochastic relaxation. These three methods have been implemented on a Connection Machine CM2. Experimental results are compared to those obtained by the Metropolis algorithm, the Gibbs sampler and ICM (Iterated Conditional Mode).
38593151
Performance Comparison of a Deterministic and a Stochastic Method for Image Classification
In this paper, we compare the performances of two methods that we proposed for image classification. The first method is a stochastic approach which uses a Markov random field modelization. An optimization technique based on the game theory is used for the maximization of the a posteriori probability of the classes. The second method is a deterministic one which uses a feed-forward neural network. A novel learning method called projection learning is employed to train the neural network. The performances of these two methods are compared through their applications to image classification. The advantages and disadvantages of both methods, as well as possibilities to improve them are discussed.
3164237
Multi-output regularized projection
Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs which is an area of growing interest. In this paper we introduce a novel projection framework which is sensitive to both input features and outputs. Based on the derived features prediction accuracy can be greatly improved. We validate our approach in two applications. The first is to model users' preferences on a set of paintings. The second application is concerned with image categorization where each image may belong to multiple categories. The proposed algorithm produces very encouraging results in both settings.
1105581
Class-is-type is inadequate for object reuse
It is well known that class and type are two different concepts in object-oriented programming languages (OOPLs). However, in many popular OOPLs, classes are used as types. In this paper, we argue that the class-is-type principle is a major obstacle to software reuse, especially to object reuse. The concepts of the basic entities, i.e., objects, object classes, object types, and object kinds, in the type hierarchy of OOPLs are revisited. The notion of object reuse is defined and elaborated. In addition, we show that parameterized types and generic functions are better served by used kind-bounded quantification than universal quantification and other mechanisms.