16393711
Salient Feature Selection for Visual Concept Learning
Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval. In this paper, we propose a novel image classification framework by learning semantic concepts of image categories. To choose representative features for an image category and meanwhile reduce noisy features, a three-step salient feature selection strategy is proposed. In the feature selection stage, salient patches are first detected and clustered. Then the region of dominance and salient entropy measures are calculated to reduce non-common salient patches for the category. Based on the selected visual keywords, SVM and keyword frequency model categorization method are applied to classification, respectively. The experimental results on Corel image database demonstrate that the proposed salient feature selection approach is very effective in image classification and visual concept learning.
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.
1573162
Automated annotation of human faces in family albums
Automatic annotation of photographs is one of the most desirable needs in family photograph management systems. In this paper, we present a learning framework to automate the face annotation in family photograph albums. Firstly, methodologies of content-based image retrieval and face recognition are seamlessly integrated to achieve automated annotation. Secondly, face annotation is formulated in a Bayesian framework, in which the face similarity measure is defined as maximum a posteriori (MAP) estimation. Thirdly, to deal with the missing features, marginal probability is used so that samples which have missing features are compared with those having the full feature set to ensure a non-biased decision. The experimental evaluation has been conducted within a family album of few thousands of photographs and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.
18096926
Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method
Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dimension reduction method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The main idea of NPP is to approximate the classical locally linear embedding (i.e. LLE) by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Preliminary experimental results on known manifold data show the effectiveness of the proposed method.
16359335
Scalable relevance feedback using click-through data for web image retrieval
Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalability, efficiency and effectiveness issues. In this paper we proposed a scalable relevance feedback mechanism using click-through data for web image retrieval. The proposed mechanism regards users' click-through data as implicit feedback which could be collected at lower cost, in larger quantities and without extra burden on the user. During RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Experimental results on a database consisting of nearly three million Web images show that the proposed mechanism is wieldy, scalable and effective.
14086659
VirtualTour: an online travel assistant based on high quality images
With the popularity of both travel and Web, more and more people use online travel services to facilitate their travel activities or share their travel experiences. Considering that existing services emphasize more on the textual content with the pictorial content only as supplement, we propose the VirtualTour system. It is an online travel service dedicated on high quality images, which helps travelers plan their trip. The images of VirtualTour are from photo forum sites. They have rich and accurate metadata which could be used to extract geographic location information of them and assess the quality of them. A representative sights identification algorithm is also proposed to automatically identify the possible related sights of a region. Based on the map services that seamlessly integrated into the system, a wieldy UI is designed to support several useful features, e.g. query by map, location or path.
205876162
Independence and connectivity in 3-domination-critical graphs
Let δ, γ, κ and α be, respectively, the minimum degree, the domination number, the con nectivity and the independence number of a graph G. The graph G is 3-domination-critical if γ = 3 and the addition of any edge decreases γ by 1. In this paper, we prove that if G is a 3-domination-critical graph, then α ≤ κ + 2; and moreover, if κ ≤ δ -1, then α ≤ κ + 1. We also give a short proof of Wojcicka's result, which says that every connected 3-domination-critical graph of order at least 7 contains a hamiltonian path (J. Graph Theory 14 (1990) 205).
7408744
Projection defocus analysis for scene capture and image display
In order to produce bright images, projectors have large apertures and hence narrow depths of field. In this paper, we present methods for robust scene capture and enhanced image display based on projection defocus analysis. We model a projector's defocus using a linear system. This model is used to develop a novel temporal defocus analysis method to recover depth at each camera pixel by estimating the parameters of its projection defocus kemel in frequency domain. Compared to most depth recovery methods, our approach is more accurate near depth discontinuities. Furthermore, by using a coaxial projector-camera system, we ensure that depth is computed at all camera pixels, without any missing parts. We show that the recovered scene geometry can be used for refocus synthesis and for depth-based image composition. Using the same projector defocus model and estimation technique, we also propose a defocus compensation method that filters a projection image in a spatially-varying, depth-dependent manner to minimize its defocus blur after it is projected onto the scene. This method effectively increases the depth of field of a projector without modifying its optics. Finally, we present an algorithm that exploits projector defocus to reduce the strong pixelation artifacts produced by digital projectors, while preserving the quality of the projected image. We have experimentally verified each of our methods using real scenes.
34237375
On-Demand Business Collaboration Enablement with Services Computing
In this talk, I will introduce a model for on-demand business process based collaboration, namely, Extended Business Collaboration (eBC), and its major characteristics: modeling, solution stack, configurable business protocol enabling framework, on-demand services deployment, as well as monitoring and analytical control. Then we present a few major research issues associated with facilitating extended business collaboration, followed by our proposed Annotated Business HyperChain technology leveraging Services computing and semantic annotation model. Services computing is the evolution of Internet computing by leveraging services oriented architecture. Services computing is mainly targeting for a new ground break technology suite – Web services and services oriented Grid/Utility Computing, Business Computing, and Autonomic Computing. A research prototype is illustrated at the end of the talk followed by some observations and discussion of open research issues requiring further exploration.
15556637
Proximity problems on moving points
A kinetic data structure for the maintenance of a multidimensional range search tree is introduced. This structure is used as a building block to obtain kinetic data structures for two classical geometric proximity problems in arbitrary dlmensions: the first structure maintains the closest pair of a set of continuously moving points, and is provably efficient. The second structure maintains a spanning tree of the moving points whose cost remains within some prescribed factor of the minimum spanning tree.
8491161
Focused named entity recognition using machine learning
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. We propose a statistical model for focused named entity recognition by converting it into a classification problem. We then study the impact of various linguistic features and compare a number of classification algorithms. From experiments on an annotated Chinese news corpus, we demonstrate that the proposed method can achieve near human-level accuracy.