18758619
IGroup: a web image search engine with semantic clustering of search results
In this demo, we present IGroup, a Web image search engine that organizes the search results into semantic clusters. Different from all existing Web image search results clustering algorithms that only cluster the top few images using visual or textual features, IGroup first identifies several query-related semantic clusters based on a key phrases extraction algorithm originally proposed for clustering general Web search results. Then, all the resulting images are separated and assigned to corresponding clusters. To make the best use of the clustering results, a new user interface is proposed. Please go to http://igroup.msra.cn for real experience.
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.
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.
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.
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.
1863221
A fast and stable approach for restoration of warped document images
We present a framework for acquiring and restoring images of warped documents to their original planar shapes. Image capturing of warped documents often results in warped images. Most digital restoration approaches make use of 2D image processing methods which are dependent on the content of the images. Some recent work attempts to remove this constraint by working directly on the 3D shape of the warped documents. In our framework, we improve on this recent approach to make it more efficient and stable. First we capture the warped surface representation through a set of active lighting from a laser range scanner onto the document surface. This gives us an accurate 3D representation of the document shape. Next by using a stick constraint and a stable numerical integrator, we can digitally flatten the 3D model to a planar shape. This simple approach avoids potential instability problem and is fast. Restoration results for a few types of warped documents are demonstrated and a significant improvement over the original images can be observed.
11022941
XML-Based Advanced UDDI Search Mechanism for B2B Integration
Exploring appropriate business applications published as Web Services in UDDI registries is a critical issue. Search for such applications should be effective in terms of time and uniformed in terms of interfaces. In this paper, an XML-based advanced UDDI exploring engine, Business Explorer for Web Services (BE4WS), that provides developers with standard interfaces for efficiently searching business and service information in single or multiple UDDI registries is presented. The engine processes the proposed UDDI Search Markup Language (USML) that specifies a search request comprising multiple queries, UDDI sources, search criteria, and aggregation operator. The core component of BE4WS, Advanced UDDI Search Engine (AUSE), aggregates search results from different UDDI registries based on the USML request and built-in intelligent search facilities with the ability to cross-reference multiple categories. An aggregation example using BE4WS is demonstrated to enable easy B2B integration.
207066659
Adaptive integration activity management for on demand business process collaboration
Abstract.Today businesses are interacting and collaborating more often in the context of a business value chain involving a variety of partners to deliver products and services. Business to business (B2B) connectivity, Enterprise Application Integration (EAI) and general business collaboration processes are becoming more critical in operating and managing efficient enterprises. The required level of inter and intra enterprise integration is generally a large undertaking and involves substantial development and customization efforts. In this paper, we propose an adaptive integration activity management approach based on web services technologies to reduce code changes associated with business process integration in a distributed collaboration environment. Specifically, we present a concept of activity chain to capture non-deterministic process flows as well as deterministic process flows in a uniform manner. Then an Activity Ontology is introduced to capture the integration requirements that include adaptation behaviors, action properties, business rules, and access control policy references. The unique ontology representation and management of the integration activities provides a uniform way to integrate additional internal and external business applications to reduce the need for pre-defined and hard-wired integration methods and to minimize code changes to existing components in an existing business collaboration and integration (B2B/EAI) infrastructure. In addition, a Petri-Net modeling exercise of the proposed integration activity management is performed to help better understand the actual system and improve the system design beforehand.
13211852
Requirements guided dynamic software clustering
In this paper, we propose a requirements guided dynamic approach to address software clustering -which aims at providing the logically meaningful and high-level decompositions of large and complex systems. In our approach, the hierarchical structure of functional requirements are constructed by a text document clustering technique named hierarchical agglomerative clustering (HAC) as a high-level skeleton to facilitate the further decomposition of source code through dynamic analysis. We also perform an experimental study based on a GNU system and present the quantitative and qualitative analysis of the experimental results.
2156360
Quantifying Path Exploration in the Internet
Previous measurement studies have shown the existence of path exploration and slow convergence in the global Internet routing system, and a number of protocol enhancements have been proposed to remedy the problem. However, existing measurements were conducted only over a small number of testing prefixes. There has been no systematic study to quantify the pervasiveness of Border Gateway Protocol (BGP) slow convergence in the operational Internet, nor any known effort to deploy any of the proposed solutions. In this paper, we present our measurement results that identify BGP slow convergence events across the entire global routing table. Our data shows that the severity of path exploration and slow convergence varies depending on where prefixes are originated and where the observations are made in the Internet routing hierarchy. In general, routers in tier-1 Internet service providers (ISPs) observe less path exploration, hence they experience shorter convergence delays than routers in edge ASs; prefixes originated from tier-1 ISPs also experience less path exploration than those originated from edge ASs. Furthermore, our data show that the convergence time of route fail-over events is similar to that of new route announcements and is significantly shorter than that of route failures. This observation is contrary to the widely held view from previous experiments but confirms our earlier analytical results. Our effort also led to the development of a path-preference inference method based on the path usage time, which can be used by future studies of BGP dynamics.