10695800
Star-Structured High-Order Heterogeneous Data Co-clustering Based on Consistent Information Theory
Heterogeneous object co-clustering has become an important research topic in data mining. In early years of this research, people mainly worked on two types of heterogeneous data (denoted by pair-wise co-clustering); while recently more and more attention was paid to multiple types of heterogeneous data (denoted by high- order co-clustering). In this paper, we studied the high- order co-clustering of objects with star-structured interrelationship, i.e., there is a central type of objects that connects the other types of objects. Actually, this case could be a very good model for many real-world applications, such as the co-clustering of Web images, their low-level visual features, and the surrounding text. We used a tripartite graph to represent the interrelationships among different objects, and proposed a consistent information theory which generates an effective algorithm to obtain the co-clusters of different types of objects. Experiments on a Web image show that our proposed algorithm is a better choice compared with previous work on heterogeneous object co-clustering.
9484829
Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization
In many applications, we need to cluster large-scale data objects. However, some recently proposed clustering algorithms such as spectral clustering can hardly handle large-scale applications due to the complexity issue, although their effectiveness has been demonstrated in previous work. In this paper, we propose a fast solver for spectral clustering. In contrast to traditional spectral clustering algorithms that first solve an eigenvalue decomposition problem, and then employ a clustering heuristic to obtain labels for the data points, our new approach sequentially decides the labels of relatively well-separated data points. Because the scale of the problem shrinks quickly during this process, it can be much faster than the traditional methods. Experiments on both synthetic data and a large collection of product records show that our algorithm can achieve significant improvement in speed as compared to traditional spectral clustering algorithms.
39165911
Constant false-alarm ratio processing for video cut detection
In this paper, a video cut detection algorithm with constant false-alarm ratio (CFAR) is proposed. Applying the non-parameter based CFAR processing technique from radar signal detection, a theoretical threshold determination strategy for video cut detection is developed, which results in controllable precision and evaluative recall performances. Detailed deductions and simulation results show that this algorithm leads to very good detection performance as compared to the previous works.
1299362
A new cut detection algorithm with constant false-alarm ratio for video segmentation
In this paper, a new cut detection algorithm with constant false-alarm ratio (CFAR) is proposed for video segmentation. In our method, a theoretical threshold determination strategy using the non-parameter based CFAR processing technique is developed to achieve a controllable precision as well as an evaluative recall performance for video cut detection. Simulation results show that this algorithm leads to very good detection performance as compared to the existing techniques. 2003 Elsevier Inc. All rights reserved.
9438831
Dynamic selection and effective compression of key frames for video abstraction
This paper reports on a new key frame based video abstraction method. With our method, a video sequence is first segmented into a number of video shots. Several key frames are selected in each shot using a dynamic selection technique. For these key frames, a motion-based clustering algorithm is applied so that key frames in the same cluster are alike in sense of motion compensation error, while those from different clusters are quit dissimilar. Then a novel cluster-based coding scheme is developed for efficient representation of the key frames. Simulations show that the proposed method can select key frames according to the dynamics of a video sequence and abstract the video with different levels of scalability.
5237024
Time-constraint boost for TV commercials detection
Commercials detection is very important for TV broadcast analysis. However, independent classification of video shots is very difficult because a considerable portion of individual commercial shots look very much like the programs. In this paper, the authors proposed a novel way to tackle this problem: to dependently treat successive video shots and improve the final classification performance by considering their temporal coherence. Following this idea, the authors discussed how to apply the majority-based windowing and minority-based merging techniques to the training and test process of statistical classifiers. As a result, a new algorithm named time-constraint boost is proposed. Simulation results show that this algorithm can improve both the training and generalization performance and lead to a promising commercials detection accuracy.
10793748
Robust face detection with multi-class boosting
With the aim to design a general learning framework for detecting faces of various poses or under different lighting conditions, we are motivated to formulate the task as a classification problem over data of multiple classes. Specifically, our approach focuses on a new multi-class boosting algorithm, called MBHboost, and its integration with a cascade structure for effectively performing face detection. There are three main advantages of using MBHboost: 1) each MBH weak learner is derived by sharing a good projection direction such that each class of data has its own decision boundary; 2) the proposed boosting algorithm is established based on an optimal criterion for multi-class classification; and 3) since MBHboost is flexible with respect to the number of classes, it turns out that it is possible to use only one single boosted cascade for the multi-class detection. All these properties give rise to a robust system to detect faces efficiently and accurately.
3193257
Sparse representations for image decomposition with occlusions
We study the problem of how to detect "interesting objects" appeared in a given image, I. Our approach is to treat it as a function approximation problem based on an over-redundant basis, and also account for occlusions, where the basis superposition principle is no longer valid. Since the basis (a library of image templates) is over-redundant, there are infinitely many ways to decompose I. We are motivated to select a sparse/compact representation of I, and to account for occlusions and noise. We then study a greedy and iterative "weighted L/sup p/ Matching Pursuit" strategy, with O<p<1. We use an L/sup p/ result to compute a solution, select the best template, at each stage of the pursuit.
61617258
Detecting deformable objects with flexible shape priors
We address the problem of detecting objects/shapes with large deformation and articulation in cluttered images. The approach requires a shape prior that describes the approximated outline and articulation property of a given model. While dynamic programming is often used in solving shape detection, our focus is on formulating a more effective energy function to evaluate the optimality of a matching between the shape prior and image features. For efficiency, the detection via optimization is carried out over a non-uniform elastic grid based on referencing the edge information. Experimental results are included to illustrate our method.
15373963
A probabilistic SVM approach for background scene initialization
Visual tracking systems using background subtraction have been very popular largely due to their efficiency in extracting moving objects. However, such systems often compute the reference background by assuming no moving objects are present during the initialization stage, though the assumption may not be realistic. We propose an automatic way to perform background initialization using a probabilistic SVM (support vector machine). By formulating the problem as an on-line classification one, our approach has the potential to be real-time. SVM classification is carried out for all elements of each image frame by computing the output probabilities. Newly found background elements are evaluated and determined if they should be added to the solution. The process of background initialization continues until there are no more new background elements to be considered. As the features used in an SVM dictate the outcome of classification, we find that optical flow value and inter-frame difference are the two most important ones. Experimental results are included to demonstrate the efficiency of our method.
763779
Trust-region methods for real-time tracking
Optimization methods based on iterative schemes can be divided into two classes: linesearch methods and trust-region methods. While linesearch techniques are commonly found in various vision applications, not much attention is paid to trust-region methods. Motivated by the fact that linesearch methods can be considered as special cases of trust-region methods, we propose to apply trust-region methods to visual tracking problems. Our approach integrates trust-region methods with the Kullback Leibler distance to track a rigid or non-rigid object in real-time. If not limited by the speed of a camera, the algorithm can achieve frame rate above 60 fps. To justify our method, a variety of experiments/comparisons are carried out for the trust-region tracker and a linesearch-based mean-shift tracker with same initial conditions. The experimental results support our conjecture that a trust-region tracker should perform superiorly to a linesearch one.