Hwann-Tzong Chen

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.

Fusing generic objectness and visual saliency for salient object detection

We present a novel computational model to explore the relatedness of objectness and saliency, each of which plays an important role in the study of visual attention. The proposed framework conceptually integrates these two concepts via constructing a graphical model to account for their relationships, and concurrently improves their estimation by iteratively optimizing a novel energy function realizing the model. Specifically, the energy function comprises the objectness, the saliency, and the interaction energy, respectively corresponding to explain their individual regularities and the mutual effects. Minimizing the energy by fixing one or the other would elegantly transform the model into solving the problem of objectness or saliency estimation, while the useful information from the other concept can be utilized through the interaction term. Experimental results on two benchmark datasets demonstrate that the proposed model can simultaneously yield a saliency map of better quality and a more meaningful objectness output for salient object detection.

Real-time tracking using trust-region methods

Optimization methods based on iterative schemes can be divided into two classes: line-search methods and trust-region methods. While line-search techniques are commonly found in various vision applications, not much attention is paid to trust-region ones. Motivated by the fact that line-search methods can be considered as special cases of trust-region methods, we propose to establish a trust-region framework for real-time tracking. Our approach is characterized by three key contributions. First, since a trust-region tracking system is more effective, it often yields better performances than the outcomes of other trackers that rely on iterative optimization to perform tracking, e.g., a line-search-based mean-shift tracker. Second, we have formulated a representation model that uses two coupled weighting schemes derived from the covariance ellipse to integrate an objects color probability distribution and edge density information. As a result, the system can address rotation and nonuniform scaling in a continuous space, rather than working on some presumably possible discrete values of rotation angle and scale. Third, the framework is very flexible in that a variety of distance functions can be adapted easily. Experimental results and comparative studies are provided to demonstrate the efficiency of the proposed method.

Semantic manifold learning for image retrieval

Learning the users semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relations specified in the feedback. Given that, we propose an augmented relation embedding (ARE) to map the image space into a semantic manifold that faithfully grasps the users preferences. Besides ARE, we also look into the issues of selecting a good feature set for improving the retrieval performance. With these two aspects of efforts we have established a system that yields far better results than those previously reported. Overall, our approach can be characterized by three key properties: 1) The framework uses one relational graph to describe the similarity relations, and the other two to encode the relevant/irrelevant relations indicated in the feedback. 2) With the relational graphs so defined, learning a semantic manifold can be transformed into solving a constrained optimization problem, and is reduced to the ARE algorithm accounting for both the representation and the classification points of views. 3) An image representation based on augmented features is introduced to couple with the ARE learning. The use of these features is significant in capturing the semantics concerning different scales of image regions. We conclude with experimental results and comparisons to demonstrate the effectiveness of our method.

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.

Preattentive co-saliency detection

This paper presents a new algorithm to solve the problem of co-saliency detection, i.e., to find the common salient objects that are present in both of a pair of input images. Unlike most previous approaches, which require correspondence matching, we seek to solve the problem of co-saliency detection under a preattentive scheme. Our algorithm does not need to perform the correspondence matching between the two input images, and is able to achieve co-saliency detection before the focused attention occurs. The joint information provided by the image pair enables our algorithm to inhibit the responses of other salient objects that appear in just one of the images. Through experiments we show that our algorithm is effective in localizing the co-salient objects inside input image pairs.

Probabilistic tracking with adaptive feature selection

We propose a color-based tracking framework that infers alternately an objects configuration and good color features via particle filtering. The tracker adaptively selects discriminative color features that well distinguish foregrounds from backgrounds. The effectiveness of a feature is weighted by the Kullback-Leibler observation model, which measures dissimilarities between the color histograms of foregrounds and backgrounds. Experimental results show that the probabilistic tracker with adaptive feature selection is resilient to lighting changes and background distractions.

Histogram-based interest point detectors

We present a new method for detecting interest points using histogram information. Unlike existing interest point detectors, which measure pixel-wise differences in image intensity, our detectors incorporate histogram-based representations, and thus can find image regions that present a distinct distribution in the neighborhood. The proposed detectors are able to capture large-scale structures and distinctive textured patterns, and exhibit strong invariance to rotation, illumination variation, and blur. The experimental results show that the proposed histogram-based interest point detectors perform particularly well for the tasks of matching textured scenes under blur and illumination changes, in terms of repeatability and distinctiveness. An extension of our method to space-time interest point detection for action classification is also presented.

Tone reproduction: a perspective from luminance-driven perceptual grouping

We address the tone reproduction problem by integrating the local adaptation effect with the consistency in global contrast impression. Many previous works on tone reproduction have focused on investigating the local adaptation mechanism of human eyes to compress high-dynamic-range (HDR) luminance into a displayable range. Nevertheless, while the realization of local adaptation properties is not theoretically defined, exaggerating such effects often leads to unnatural visual impression of global contrast. We propose to perceptually decompose the luminance into a small number of regions that sequentially encode the overall impression of an HDR image. A piecewise tone mapping can then be constructed to region-wise perform HDR compressions, using local mappings constrained by the estimated global perception. Indeed, in our approach, the region information is used not only to practically approximate the local properties of luminance, but more importantly to retain the global impression. Besides, it is worth mentioning that the proposed algorithm is efficient, and mostly does not require excessive parameter fine-tuning. Our experimental results and comparisons indicate that the described framework gives a good balance in both preserving local details and maintaining global perceptual impression of HDR scenes.

Video object cosegmentation

We introduce and address the problem of video object cosegmentation, which concerns the task of segmenting the common object in a pair of video sequences. We present a new algorithm that works on super-voxels in videos to solve this task. The algorithm computes i the intra-video relative motion derived from dense optical flow and ii) the inter-video co-features based on Gaussian mixture models. The experimental results show that, by integrating the intra-video and inter-video information, our algorithm is able to obtain better results of segmenting video objects.