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.
20330655
Site abstraction for rare category classification in large-scale web directory
Automatically classifying the Web directories is an effective way to manage Web information. However, our experiments showed that the state-of-the-art text classification technologies could not lead to acceptable performance in this task. Due to our analysis, the main problem is the lack of effective training data in rare categories of Web directories. To tackle this problem, we proposed a novel technology named Site Abstraction to synthesize new training examples from the website of the existing training document. The main idea is to propagate features through parent-child relationship in the sitemap tree. Experiments showed that our method significantly improved the classification performance.
1733796
Event detection from evolution of click-through data
Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data. In this paper, we propose the first approach to detect events from the click-through data, which is the log data of web search engines. The intuition behind event detection from click-through data is that such data is often event-driven and each event can be represented as a set ofquery-page pairs that are not only semantically similar but also have similar evolution pattern over time. Given the click-through data, in our proposed approach, we first segment it into a sequence of bipartite graphs based on theuser-defined time granularity. Next, the sequence of bipartite graphs is represented as a vector-based graph, which records the semantic and evolutionary relationships between queries and pages. After that, the vector-based graph is transformed into its dual graph, where each node is a query-page pair that will be used to represent real world events. Then, the problem of event detection is equivalent to the problem of clustering the dual graph of the vector-based graph. The clustering process is based on a two-phase graph cut algorithm. In the first phase, query-page pairs are clustered based on thesemantic-based similarity such that each cluster in the result corresponds to a specific topic. In the second phase, query-page pairs related to the same topic are further clustered based on the evolution pattern-based similarity such that each cluster is expected to represent a specific event under the specific topic. Experiments with real click-through data collected from a commercial web search engine show that the proposed approach produces high quality results.
7331201
Webpage importance analysis using conditional Markov random walk
In this paper, we propose a novel method to calculate the Web page importance based on a conditional Markov random walk model. The main assumption in this model is that given the hyperlinks in a Web page, users are not really randomly clicking one of them. Instead, many factors may bias their behaviors, for example, the anchor text, the content relevance and the previous experiences when visiting the Web site that a destination page belongs to. As one of the results, the user might tend to visit those pages in high-quality Web sites with higher probability. To implement this idea, we reformulate the Web graph to be a two-layer structure, and the Web page importance is calculated by conditional random walk in this new Web graph. Experiments on the topic distillation task of TREC 2003 Web track showed that our new method can achieve about 18% improvement on mean average precision (MAP) and 16% on precision at 10 (P@10) over the PageRank algorithm.
21449211
Effective Feature Extraction for Play Detection in American Football Video
The fact that a typical broadcast can last over 3 hours for a game of 60 minutes makes video summarization of American football games most desirable. In this paper, we present several feature extraction methods for play detection in American football video. Wavelet based motion analysis is used to extract the trend component from the noisy motion vectors; a hybrid field-color model detects field area with both high accuracy and fast speed; and a prior knowledge driven line detection method uses the court information to estimate miss-detections. Based on the so-extracted features, a boosting chain is used for feature selection and decision making. Tested on large-size video data, the detection performance of our work is very promising.
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.
1579121
Representation and self-similarity of shapes
Representing shapes is a significant problem for vision systems that must recognize or classify objects. We derive a representation for a given shape by investigating its self-similarities, and constructing its shape axis (SA) and shape axis tree (SA-tree). We start with a shape, its boundary contour, and two different parameterizations for the contour. To measure its self-similarity we consider matching pairs of points (and their tangents) along the boundary contour, i.e., matching the two parameterizations. The matching, of self-similarity criteria may vary, e.g., co-circularity, parallelism, distance, region homogeneity. The loci of middle points of the pairing contour points are the shape axis and they can be grouped into a unique tree graph, the SA-tree. The shape axis for the co-circularity criteria is compared to the symmetry axis. An interpretation in terms of object parts is also presented.
5625072
Segmenting Highly Articulated Video Objects with Weak-Prior Random Forests
We address the problem of segmenting highly articulated video objects in a wide variety of poses. The main idea of our approach is to model the prior information of object appearance via random forests. To automatically extract an object from a video sequence, we first build a random forest based on image patches sampled from the initial template. Owing to the nature of using a randomized technique and simple features, the modeled prior information is considered weak, but on the other hand appropriate for our application. Furthermore, the random forest can be dynamically updated to generate prior probabilities about the configurations of the object in subsequent image frames. The algorithm then combines the prior probabilities with low-level region information to produce a sequence of figure-ground segmentations. Overall, the proposed segmentation technique is useful and flexible in that one can easily integrate different cues and efficiently select discriminating features to model object appearance and handle various articulations.
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.
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.
122073
Fast Object Detection with Occlusions
We describe a new framework, based on boosting algorithms and cascade structures, to efficiently detect objects/faces with occlusions. While our approach is motivated by the work of Viola and Jones, several techniques have been developed for establishing a more general system, including (i) a robust boosting scheme, to select useful weak learners and to avoid overfitting; (ii) reinforcement training, to reduce false-positive rates via a more effective training procedure for boosted cascades; and (iii) cascading with evidence, to extend the system to handle occlusions, without compromising in detection speed. Experimental results on de- tecting faces under various situations are provided to demonstrate the performances of the proposed method.