Detecting unusual activity in video
We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selections are used. We divide the video into equal length segments and classify the extracted features into prototypes, from which a prototype-segment co-occurrence matrix is computed. Motivated by a similar problem in document-keyword analysis, we seek a correspondence relationship between prototypes and video segments which satisfies the transitive closure constraint. We show that an important sub-family of correspondence functions can be reduced to co-embedding prototypes and segments to N-D Euclidean space. We prove that an efficient, globally optimal algorithm exists for the co-embedding problem. Experiments on various real-life videos have validated our approach.

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: Object recognition using boosted discriminants
abstract: We approach the task of object discrimination as that of learning efficient "codes" for each object class in terms of responses to a set of chosen discriminants. We formulate this approach in an energy minimization framework. The "code" is built incrementally by successively constructing discriminants that focus on pairs of training images of objects that are currently hard to classify. The particular discriminants that we use partition the set of objects of interest into two well-separated groups. We find the optimal discriminant as well as partition by formulating an objective criteria that measures the well-separateness of the partition. We derive an iterative solution that alternates between the solutions for two generalized eigenproblems, one for the discriminant parameters and the other for the indicator variables denoting the partition. We show how the optimization can easily be biased to focus on hard to classify pairs, which enables us to choose new discriminants one by one in a sequential manner We validate our approach on a challenging face discrimination task using parts as features and show that it compares favorably with the performance of an eigenspace method.

title: Motion segmentation and tracking using normalized cuts
abstract: We propose a motion segmentation algorithm that aims to break a scene into its most prominent moving groups. A weighted graph is constructed on the image sequence by connecting pixels that are in the spatiotemporal neighborhood of each other. At each pixel, we define motion profile vectors which capture the probability distribution of the image velocity. The distance between motion profiles is used to assign a weight on the graph edges. Using normalised cuts we find the most salient partitions of the spatiotemporal graph formed by the image sequence. For segmenting long image sequences, we have developed a recursive update procedure that incorporates knowledge of segmentation in previous frames for efficiently finding the group correspondence in the new frame.

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: 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: Left cells containing a fully commutative element
abstract: Let W be a finite or an affine Coxeter group and Wc the set of all the fully commutative elements in W. For any left cell L of W containing some fully commutative element, our main result of the paper is to prove that there exists a unique element (say wL) in L ∩ Wc such that any z ∈ L has the form z = xwL with l(z) = l(x) + l(wL) for some x ∈ W. This implies that L is left connected, verifying a conjecture of Lusztig in our case.

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: Sign Types Associated to Posets
abstract: We start with a combinatorial definition of I-sign types which are a generalization of the sign types indexed by the root system of type Al (I?N finite). Then we study the set DIp of I-sign types associated to the partial orders on I. We establish a 1?1 correspondence between D[n]p and a certain set of polyhedral cones in a euclidean space by which we get a geometric distinction of the sign types in D[n]p from the other [n]-sign types. We give a graph-theoretical criterion for an Sn-orbit O of D[n]p to contain a dast and show that O contains at most one dast. Finally, we show the admirability of a poset associated to a dast.

title: Perspectives on cognitive informatics
abstract: From a scientific perspective explaining how the brain thinks is a big goal. Cognitive informatics studies intelligent behavior from a computational point of view in terms of updated research efforts and processes of brain science and neuroscience. Cognitive informatics is the interdisciplinary study of cognition. Cognition includes mental states and processes, such as thinking, reasoning, remembering, language understanding and generation, visual and auditory perception, learning, consciousness, emotions, etc. In this paper we will point out basic research topics of learning, memory, thought, language, and neural computing which are active fields related to cognitive informatics.

title: How effective are landmarks and their geometry for face recognition?
abstract: This paper evaluates how biologically meaningful landmarks and their geometry extracted from face images can be used for face recognition. The traditional Procrustes distance is studied for the landmark-based face model. By using complex principal component analysis, we propose a refined Procrustes distance that incorporates statistical correlation of landmarks. Motivated by research results from art, anthropology, and aesthetic surgery where ratios play a significant role in human face descriptions, we also investigate how well-normalized Euclidean distances (special ratios) can be exploited for face recognition. Exploiting symmetry and using principal component analysis, we reduce the number of ratios to 20. In this investigation, we analyze the effectiveness of three well-known similarity measures including the typical l"1 norm, l"2 norm, and Mahalanobis distance. We also define a new similarity measure called the eigenvalue-weighted cosine (EWC) distance. We evaluate our approach using two standard face databases: the Purdue AR and the FERET database. The former has well-controlled face images and we use it to test the reliability of landmarks. The FERET database is used for performance evaluation of face recognition algorithms. This papers use a performance measure called the cumulative match score, which indicates the percentage of faces that can be eliminated from consideration. Experimental results show that our EWC distance outperforms the l"1 and l"2 norms and even the Mahalanobis distance. Our study also finds that landmarks and their geometry-based approach can account for variations of face expression and aging very well. Thus, they can be used either in stand-alone mode or in conjunction with other approaches to reduce the search space a priori.