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.
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.
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.
43152561
Neural Network Based Online Feature Selection for Vehicle Tracking
Aiming at vehicle tracking with a single moving camera for autonomous driving, this paper presents a strategy of online feature selection combined with related process framework. Detected vehicle can provide more information for tracking. A principal component analysis neural network is used to select appearance features online. Then the positive and negative histogram models using selected features are found for the detected vehicle and the surroundings. A likelihood function is defined based on histogram models, and it can be used as a simple classifier. For selected multiple features, the corresponding multiple classifiers are combined with a single layer perceptron. Experimental results indicate the validity and real-time performance.
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.
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.
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.
2588176
Local discriminant embedding and its variants
We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data are used to construct the embedding for classification problems. The proposed algorithm learns the embedding for the submanifold of each class by solving an optimization problem. After being embedded into a low-dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring points of different classes no longer stick to one another. Via embedding, new test data are thus more reliably classified by the nearest neighbor rule, owing to the locally discriminating nature. We also describe two useful variants: two-dimensional LDE and kernel LDE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method.