18011307
Subspace Clustering and Label Propagation for Active Feedback in Image Retrieval
In recent years, relevance feedback has been studied extensively as a way to improve performance of content-based image retrieval (CBIR). However, since users are usually unwilling to provide many feedbacks, the insufficiency of the training samples limited the success of relevance feedback. To tackle this problem, we propose two coupled algorithms: (i) overlapped subspace clustering to select representative images for user’s feedback; and (ii) multi-subspace label propagation to include unlabeled data in the training process. As these two algorithms are both working on sub feature spaces of the image database, they can not only deal with the insufficient training samples but also well capture the user’s attention during the retrieval process. Experimental results on a large database of general-purposed images demonstrated the high effectiveness of our proposed 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.
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.
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.
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.
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.
30641699
Tone Reproduction: A Perspective from Luminance-Driven Perceptual Grouping
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.
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.
18846553
Visual Deconstruction: Recognizing Articulated Objects
We propose a deconstruction framework to recognize and find articulated objects. In particular we are interested in human arm and leg articulations.
6416068
Shape recognition using fast boosted filtering
We address the problem of recognizing 2-D shapes in images via multi-class classifications. Our approach has three key elements. First, a signed distance transform is introduced to represent a shape more informatively. Second, a filter bank is generated such that its filters can capture multiple-scale local and global features between two shapes of different classes. We then apply boosting to combine useful filters to construct discriminant classifiers. Third, in implementing our system, a new classification architecture is developed to accomplish multi-class recognition. To examine the claimed efficiencies, we consider an example of document recognition by pinpointing the strengths of our method through experimental results and comparisons.