14488825
Image annotation using search and mining technologies
In this paper, we present a novel solution to the image annotation problem which annotates images using search and data mining technologies. An accurate keyword is required to initialize this process, and then leveraging a large-scale image database, it 1) searches for semantically and visually similar images, 2) and mines annotations from them. A notable advantage of this approach is that it enables unlimited vocabulary, while it is not possible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.
15083897
Grouping web image search result
In this paper, we propose a Web image search result organizing method to facilitate user browsing. We formalize this problem as a salient image region pattern extraction problem. Given the images returned by Web search engine, we first segment the images into homogeneous regions and quantize the environmental regions into image codewords. The salient codeword "phrases" are then extracted and ranked based on a regression model learned from human labeled training data. According to the salient "phrases", images are assigned to different clusters, with the one nearest to the centroid as the entry for the corresponding cluster. Satisfying experimental results show the effectiveness of our proposed method.
32712892
Learning Image Manifold Using Web Data
Manifold learning has become a hot research topic in recent years and is widely used in the area of dimension reduction, information retrieval and ranking, etc. However, how to reconstruct the intrinsic manifold from the observed data points, i.e. what is the proper data point distance measure, is still an open problem. In this paper, we propose to take advantages of the information provided by web-pages and the image-related website link structure to learn the Web image manifold, which better approaches to the intrinsic manifold than those learned by previous methods which use Euclidean alike distances to construct the initial affinity matrix. Experimental results prove the effectiveness of our learned Web image manifold.
15373083
WSMO-PA: Formal Specification of Public Administration Service Model on Semantic Web Service Ontology
In this paper we define a formal model for a public administration service on the basis of the Web service modeling ontology (WSMO). For this purpose we employ the generic public service object model of the governance enterprise architecture (GEA) providing a PA domain specific semantics. We investigate conceptual mappings between PA entities and WSMO elements, and on the real-world use case present the detailed formal PA service model based on the WSMO service model
612178
A learning-based approach to estimate statistics of operators in continuous queries: a case study
Statistic estimation such as output size estimation of operators is a well-studied subject in the database research community, mainly for the purpose of query optimization. The assumption, however, is that queries are ad-hoc and therefore the emphasis has been on capturing the data distribution. When long standing continuous queries on a changing database are concerned, a more direct approach, namely building an estimation model for each operator, is possible. In this paper, we propose a novel learning-based method. Our method consists of two steps. The first step is to design a dedicated feature extraction algorithm that can be used incrementally to obtain feature values from the underlying data. The second step is to use a data mining algorithm to generate an estimation model based on the feature values extracted from the historical data. To illustrate the approach, this paper studies the case of similarity-based searches over streaming time series. Experimental results show this approach provides accurate statistic estimates with a low overhead.
149826
Generalized component analysis for text with heterogeneous attributes
We present a class of richly structured, undirected hidden variable models suitable for simultaneously modeling text along with other attributes encoded in different modalities. Our model generalizes techniques such as principal component analysis to heterogeneous data types. In contrast to other approaches, this framework allows modalities such as words, authors and timestamps to be captured in their natural, probabilistic encodings. A latent space representation for a previously unseen document can be obtained through a fast matrix multiplication using our method. We demonstrate the effectiveness of our framework on the task of author prediction from 13 years of the NIPS conference proceedings and for a recipient prediction task using a 10-month academic email archive of a researcher. Our approach should be more broadly applicable to many real-world applications where one wishes to efficiently make predictions for a large number of potential outputs using dimensionality reduction in a well defined probabilistic framework.
13915062
Intrapersonal subspace analysis with application to adaptive Bayesian face recognition
We propose a subspace distance measure to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace the average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. We call an intrapersonal subspace specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies from person to person and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods when a small number of adaptation images are available.
34417323
Specification Methodology: An Integrated Relational Approach
The effectiveness of set theoretic concepts for the purpose of software specification is becoming more and more widely recognized. This paper presents an integrated relational methodology for software specification, and shows its effectivness when applied to a practical example.