Segmentation given partial grouping constraints
We consider data clustering problems where partial grouping is known a priori. We formulate such biased grouping problems as a constrained optimization problem, where structural properties of the data define the goodness of a grouping and partial grouping cues define the feasibility of a grouping. We enforce grouping smoothness and fairness on labeled data points so that sparse partial grouping information can be effectively propagated to the unlabeled data. Considering the normalized cuts criterion in particular, our formulation leads to a constrained eigenvalue problem. By generalizing the Rayleigh-Ritz theorem to projected matrices, we find the global optimum in the relaxed continuous domain by eigendecomposition, from which a near-global optimum to the discrete labeling problem can be obtained effectively. We apply our method to real image segmentation problems, where partial grouping priors can often be derived based on a crude spatial attentional map that binds places with common salient features or focuses on expected object locations. We demonstrate not only that it is possible to integrate both image structures and priors in a single grouping process, but also that objects can be segregated from the background without specific object knowledge.

title: Understanding popout through repulsion
abstract: Perceptual popout is defined by both feature similarity and local feature contrast. We identify these two measures with attraction and repulsion, and unify the dual processes of association by attraction and segregation by repulsion in a single grouping framework. We generalize normalized cuts to multi-way partitioning with these dual measures. We expand graph partitioning approaches to weight matrices with negative entries, and provide a theoretical basis for solution regularization in such algorithms. We show that attraction, repulsion and regularization each contributes in a unique way to popout. Their roles are demonstrated in various salience detection and visual search scenarios. This work opens up the possibilities of encoding negative correlations in constraint satisfaction problems, where solutions by simple and robust eigendecomposition become possible.

title: Grouping with Bias
abstract: With the optimization of pattern discrimination as a goal, graph partitioning approaches often lack the capability to integrate prior knowledge to guide grouping. In this paper, we consider priors from unitary generative models, partially labeled data and spatial attention. These priors are modelled as constraints in the solution space. By imposing uniformity condition on the constraints, we restrict the feasible space to one of smooth solutions. A subspace projection method is developed to solve this constrained eigenproblem. We demonstrate that simple priors can greatly improve image segmentation results.

title: Silhouette-based human identification from body shape and gait
abstract: Our goal is to establish a simple baseline method for human identification based on body shape and gait. This baseline recognition method provides a lower bound against which to evaluate more complicated procedures. We present a viewpoint-dependent technique based on template matching of body silhouettes. Cyclic gait analysis is performed to extract key frames from a test sequence. These frames are compared to training frames using normalized correlation, and subject classification is performed by nearest-neighbor matching among correlation scores. The approach implicitly captures biometric shape cues such as body height, width, and body-part proportions, as well as gait cues such as stride length and amount of arm swing. We evaluate the method on four databases with varying viewing angles, background conditions (indoors and outdoors), walking styles and pixels on target.

title: Segmentation with Pairwise Attraction and Repulsion
abstract: We propose a method of image segmentation by integrating pairwise attraction and directional repulsion derived from local grouping and figure-ground cues. These two kinds of pairwise relationships are encoded in the real and imaginary parts of an Hermitian graph weight matrix, through which we can directly generalize the normalized cuts criterion. With bi-graph constructions, this method can be readily extended to handle nondirectional repulsion that captures dissimilarity. We demonstrate the use of repulsion in image segmentation with relative depth cues, which allows segmentation and figure-ground segregation to be computed simultaneously. As a general mechanism to represent the dual measures of attraction and repulsion, this method can also be employed to solve other constraint satisfaction and optimization problems.

title: Normalized cuts and image segmentation
abstract: We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images and found results very encouraging.

title: Spatially Constrained Wiener Filter with Markov Autocorrelation Modeling for Image Resolution Enhancement
abstract: This paper develops a practical method for image resolution enhancement. The method optimizes the spatially constrained Wiener filter for an efficiently parameterized model of the image autocorrelation based on a Markov random field (MRF) with affine transformation. The paper presents a closed-form solution to parameterize the model for an image. The enhancement method is computationally efficient, because it is formulated as convolution with a small kernel. Because the kernel is small, it can be optimized efficiently and only a small portion of the MRF autocorrelation model is required. Because the autocorrelation model parameters and optimal filter can be computed quickly, the method can be optimized locally for adaptive processing. Experimental results indicate that the new method can balance the error-budget tradeoff between signal error and aliasing error.

title: Starvation mitigation through multi-channel coordination in CSMA multi-hop wireless networks
abstract: Existing multi-channel protocols have been demonstrated to significantly increase aggregate throughput compared to single-channel protocols. However, we show that despite such improvements in aggregate throughput, existing protocols can lead to flow starvation in a multi-hop network, a phenomenon that also occurs with single-channel protocols. In this paper, we devise Asynchronous Multi-channel Coordination Protocol (AMCP), a distributed medium access protocol that not only increases aggregate throughput, but more importantly, addresses the fundamental coordination problems that lead to starvation. Based on AMCP's counter-starvation mechanisms, we analytically derive and experimentally validate an ap-proximate lower bound on the throughput of any flow in an arbitrary topology. We also demonstrate that AMCP can deliver significantly higher per-flow throughput than both IEEE 802.11 and existing multi-channel solutions. In addition to its performance properties, AMCP is both simple in that it operates using the primitives of IEEE 802.11 DCF, and cost-effective in that it requires only a single half-duplex transceiver and no infrastructure support.

title: Face recognition using landmark-based bidimensional regression
abstract: This paper studies how biologically meaningful landmarks extracted from face images can be exploited for face recognition using the bidimensional regression. Incorporating the correlation statistics of landmarks, this paper also proposes a new approach called eigenvalue weighted bidimensional regression. Complex principal component analysis is used for computing eigenvalues and removing correlation among landmarks. We evaluate our approach using two standard face databases: the Purdue AR and the NIST FERET. Experimental results show that the bidimensional regression is an efficient method to exploit geometry information of face images.

title: Image interpolation by two-dimensional parametric cubic convolution
abstract: Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2,2]/spl times/[-2,2] with constraints for biaxial symmetry, diagonal (or 90/spl deg/ rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the constraint of diagonal symmetry, based on the observation that many images have rotationally asymmetric statistical properties. This paper also develops a closed-form solution for determining the optimal parameter values for parametric cubic-convolution kernels with respect to ensembles of scenes characterized by autocorrelation (or power spectrum). This solution establishes a practical foundation for adaptive interpolation based on local autocorrelation estimates. Quantitative fidelity analyses and visual experiments indicate that these new methods can outperform several popular interpolation methods. An analysis of the error budgets for reconstruction error associated with blurring and aliasing illustrates that the methods improve interpolation fidelity for images with aliased components. For images with little or no aliasing, the methods yield results similar to other popular methods. Both 2D-3PCC and 2D-5PCC are low-order polynomials with small spatial support and so are easy to implement and efficient to apply.

title: Swarm Intelligence Clustering Algorithm Based on Attractor
abstract: Ant colonies behavior and their self-organizing capabilities have been popularly studied, and various swarm intelligence models and clustering algorithms also have been proposed. Unfortunately, the cluster number is often too high and convergence is also slow. We put forward a novel structure-attractor, which actively attracts and guides the ant's behavior, and implement an efficient strategy to adaptively control the clustering behavior. Our experiments show that swarm intelligence clustering algorithm based on attractor (SICABA for short) greatly improves the convergence speed and clustering quality compared with LF and also has many notable virtues such as flexibility, decentralization compared with conventional algorithms.