214819
Scalable search-based image annotation of personal images
With the prevalence of digital cameras, more and more people have considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, for the annotated keywords could facilitate the search processes. Although many image annotation methods have been proposed in recent years, their effectiveness on arbitrary personal images is constrained by their limited scalability, i.e. limited lexicon of small-scale training set. To be scalable, we propose a search-based image annotation (SBIA) algorithm that is analogous to Web page search. First, content-based image retrieval (CBIR) technology is used to retrieve a set of visually similar images from a large-scale Web image set. Then, a text-based keyword search (TBKS) technique is used to obtain a ranked list of candidate annotations for each retrieved image. Finally, a fusion algorithm is used to combine the ranked lists into the final annotation list. The application of both efficient search technologies and Web-scale image set guarantees the scalability of the proposed algorithm. Experimental results on U. Washington dataset show not only the effectiveness and efficiency of the proposed algorithm but also the advantage of image retrieval using annotation results over that using visual features.
1857778
Boosting image orientation detection with indoor vs. outdoor classification
Automatic detection of image orientation is a very important operation in photo image management. In this paper, we propose an automated method based on the boosting algorithm to estimate image orientations. The proposed method has the capability of rejecting images based on the confidence score of the orientation detection. Also, images are classified into indoor and outdoor, and this classification result is used to further refine the orientation detection. To select features more sensitive to the rotation, we combine the features by subtraction operation and select the most useful features by boosting algorithm. The proposed method has several advantages: small model size, fast classification speed, and effective rejection scheme.
4535096
Coupled kernel-based subspace learning
It was prescriptive that an image matrix was transformed into a vector before the kernel-based subspace learning. In this paper, we take the kernel discriminant analysis (KDA) algorithm as an example to perform kernel analysis on 2D image matrices directly. First, each image matrix is decomposed as the product of two orthogonal matrices and a diagonal one by using singular value decomposition; then an image matrix is expanded to be of higher or even infinite dimensions by applying the kernel trick on the column vectors of the two orthogonal matrices; finally, two coupled discriminative kernel subspaces are iteratively learned for dimensionality reduction by optimizing the Fisher criterion measured by Frobenius norm. The derived algorithm, called coupled kernel discriminant analysis (CKDA), effectively utilizes the underlying spatial structure of objects and the discriminating information is encoded in two coupled kernel subspaces respectively. The experiments on real face databases compared with KDA and Fisherface validate the effectiveness of CKDA.
6781663
Parallel Image Matrix Compression for Face Recognition
The canonical face recognition algorithm Eigenface and Fisherface are both based on one dimensional vector representation. However, with the high feature dimensions and the small training data, face recognition often suffers from the curse of dimension and the small sample problem. Recent research [4] shows that face recognition based on direct 2D matrix representation, i.e. 2DPCA, obtains better performance than that based on traditional vector representation. However, there are three questions left unresolved in the 2DPCA algorithm: I ) what is the meaning of the eigenvalue and eigenvector of the covariance matrix in 2DPCA; 2) why 2DPCA can outperform Eigenface; and 3) how to reduce the dimension after 2DPCA directly. In this paper, we analyze 2DPCA in a different view and proof that is 2DPCA actually a "localized" PCA with each row vector of an image as object. With this explanation, we discover the intrinsic reason that 2DPCA can outperform Eigenface is because fewer feature dimensions and more samples are used in 2DPCA when compared with Eigenface. To further reduce the dimension after 2DPCA, a two-stage strategy, namely parallel image matrix compression (PIMC), is proposed to compress the image matrix redundancy, which exists among row vectors and column vectors. The exhaustive experiment results demonstrate that PIMC is superior to 2DPCA and Eigenface, and PIMC+LDA outperforms 2DPC+LDA and Fisherface.
206501510
Head pose estimation using Fisher Manifold learning
Here, we propose a new learning strategy for head pose estimation. Our approach uses nonlinear interpolation to estimate the head pose using the learning result from face images of two head poses. Advantage of our method to regression method is that it only requires training images of two head poses and better generalization ability. It outperforms existed methods, such as regression and multiclass classification method, on both synthesis and real face images. Average head pose estimation error of yaw rotation is about 4/sup 0/, which proves that our method is effective in head pose estimation.
2157887
Concurrent subspaces analysis
A representative subspace is significant for image analysis, while the corresponding techniques often suffer from the curse of dimensionality dilemma. In this paper, we propose a new algorithm, called concurrent subspaces analysis (CSA), to derive representative subspaces by encoding image objects as 2/sup nd/ or even higher order tensors. In CSA, an original higher dimensional tensor is transformed into a lower dimensional one using multiple concurrent subspaces that characterize the most representative information of different dimensions, respectively. Moreover, an efficient procedure is provided to learn these subspaces in an iterative manner. As analyzed in this paper, each sub-step of CSA takes the column vectors of the matrices, which are acquired from the k-mode unfolding of the tensors, as the new objects to be analyzed, thus the curse of dimensionality dilemma can be effectively avoided. The extensive experiments on the 3/sup rd/ order tensor data, simulated video sequences and Gabor filtered digital number image database show that CSA outperforms principal component analysis in terms of both reconstruction and classification capability.
760636
Accelerated occlusion culling using shadow frusta
Many applications in computer graphics and virtual environments need to render datasets with large numbers of primitives and high depth complexity at interactive rates. However, standard techniques like view frustum culling and a hardware z-bu er are unable to display datasets composed of hundred of thousands of polygons at interactive frame rates on current high-end graphics systems. We add a \conservative"visibility culling stage to the rendering pipeline, attempting to identify and avoid processing of occluded polygons. Given a moving viewpoint, the algorithm dynamically chooses a set of occluders. Each occluder is used to compute a shadow frustum, and all primitives contained within this frustumare culled. The algorithmhierarchicallytraverses the model, culling out parts not visible from the current viewpoint using e cient, robust, and in some cases specialized interference detection algorithms. The algorithm's performance varies with the location of the viewpoint and the depth complexity of the model. In the worst case it is linear in the input size with a small constant. In this paper, we demonstrate its performance on a city model composed of 500;000 polygons and possessing varying depth complexity. We are able to cull an average of 55% of the polygons that would not be culled by view-frustum culling and obtain a commensurate improvement in frame rate. The overall approach is e ective and scalable, is applicable to all polygonal models, and can be easily implemented on top of view-frustum culling.
1131614
Meta-Evaluation of Image Segmentation Using Machine Learning
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods have the advantage that they do not require a manually-segmented reference image for comparison, and can therefore be used for real-time evaluation. Current stand-alone evaluation methods often work well for some types of images, but poorly for others. We propose a meta-evaluation method in which any set of base evaluation methods are combined by a machine learning algorithm that coalesces their evaluations based on a learned weighting function, which depends upon the image to be segmented. The training data used by the machine learning algorithm can be labeled by a human, based on similarity to a human-generated reference segmentation, or based upon system-level performance. Experimental results demonstrate that our method performs better than the existing stand-alone segmentation evaluation methods.
1576738
Local image representations using pruned salient points with applications to CBIR
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). The features for a salient point should represent the local characteristic of that point so that the similarity between features indicates the similarity between the salient points. Traditional uses of salient points for CBIR assign features to a salient point based on the image features of all pixels in a window around that point. However, since salient points are often on the boundary of objects, the features assigned to a salient point often involve pixels from different objects. In this paper, we propose a CBIR system that uses a novel salient point method that both reduces the number of salient points using a segmentation as a filter, and also improves the representation so that it is a more faithful representation of a single object (or portion of an object) that includes information about its surroundings. We also introduce an improved Expectation Maximization-Diverse Density (EM-DD) based multiple-instance learning algorithm. Experimental results show that our CBIR techniques improve retrieval performance by 5%-11% as compared with current methods.
686393
Accelerated walkthrough of large spline models
CAD and rmimation applications. In this pa-per, we present algorithms for interactive walkthrough ofcomplex NURBS models composed of tens of thousands ofpatches on current graphics systems. Given a spline model,the algorithm precomputes simplification of a collection ofpatches and represents them hierarchically. Given a chang-ing viewpoint, the algorithm combines these simplificationswith dynamic tessellations to generate appropriate levels ofdetail. We also propose a system pipeline for parallel im-plementation on multi-processor configurations. Diiferentcomponents,
1774011
TCP connection game: a study on the selfish behavior of TCP users
We present a game-theoretic study of the selfish behavior of TCP users when they are allowed to use multiple concurrent TCP connections so as to maximize their goodputs or other utility functions. We refer to this as the TCP connection game. A central question we ask is whether there is a Nash equilibrium in such a game, and if it exists, whether the network operates efficiently at such a Nash equilibrium. Combined with the well known PFTK TCP model (1998), we study this question for three utility functions that differ in how they capture user behavior. The bad news is that the loss of efficiency or price of anarchy can be arbitrarily large if users have no resource limitations and are not socially responsible. The good news is that, if either of these two factors is considered, efficiency loss is bounded. This may partly explain why there will be no congestion collapse if many users use multiple connections.