15719994
IGroup: presenting web image search results in semantic clusters
Current web image search engines still rely on user typing textual description: query word(s) for visual targets. As the queries are often short, general or even ambiguous, the images in resulting pages vary in content and style. Thus, browsing with these results is likely to be tedious, frustrating and unpredictable.
 IGroup, a proposed image search engine addresses these problems by presenting the result in semantic clusters. The original result set was clustered in semantic groups with a cluster name relevant to user typed queries. Instead of looking through the result pages or modifying queries, IGroup users can refine findings to the interested sub-result sets with a navigational panel, where each cluster (sub-result set) was listed with a cluster name and representative thumbnails of the cluster.
 We compared IGroup with a general web image search engine: MSN, in term of efficiency, coverage, and satisfaction with a substantial user study. Our tool shows significant improvement in such criteria.
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.
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.
1882847
User Feedback-Based Refinement for Web Services Retrieval using Multiple Instance Learning
A critical step in the process of reusing existing WSDL-specified components is the discovery of potentially relevant Web services. Traditional category based Web service retrieval usually can achieve good recall but worse precision because some semantically relevant Web services are not actually relevant as they cannot provide suitable interfaces. In this paper, we present an interactive Web services retrieval mechanism to refine the coarse retrieval results set in category based retrieval. In the refinement, the signature matching of Web services that concerning the structure of operation specifications is investigated from a multi-instances view. In detail, each Web service is represented as a bag in multiple instance learning, while each operation in this Web service is regarded as an instance. This representation lies in that a user regards a service as useful if at least one operation provided by this Web service is useful. Experimental results show that our approach can improve the retrieval performance significantly: It can gain 83% precision in average after two rounds of user relevance feedback
41094552
The Statistical Inference Method in Heuristic Search Techniques
In this paper we present a new heuristic searching algorithm by introducing statistical inference method on the basic of algorithm A*. It's called algorithm SA*. The following results have been proved. (1) Algorithm SA* in superior to algorithm A*. (2) The mean complexity of SA* is CN2, but in some case A* exhibits exponential complexity (eN). (3) In a (N,d,F)- game tree, the mean complexity of SA* is CN2, but the complexity of other known game-searching algoritham (α-β , SSS* etc.) is at least dN. (4) The maximal storage-space required by SA* is C,N. 
 
This shows that under a given significance level SA* is superior to other known algorithm (e.g. A*, B*, α-β, SSS* etc.).
14817235
Placement Algorithm in Analog-Layout Designs
Analog macrocell placement is an NP-hard problem. This paper presents an attempt to solve this problem by using the optimization flow of a genetic algorithm (GA) enhanced by simulated annealing (SA). The bit-matrix representation is employed to improve the search efficiency. In particular, to reduce the solution space without degrading search opportunities, the technique of cell slide is deployed to transform an absolute placement to a relative placement. Following this cell-slide process, it is proved that, for an initial placement, there always exists a solution that can guarantee no occurrence of overlaps among cells and meet any applicable symmetry constraints pertaining to analog layouts. For the optimization of the algorithm parameters, the fractional factorial experiment using an orthogonal array has been conducted, and the exact parameter values are determined using a meta-GA approach. The experimental results show that, compared with the SA approach, the proposed algorithm consumes less computation time while generating higher quality layouts, comparable to expert manual placements
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.
12034638
A block-based inter-band lossless hyperspectral image compressor
We propose a hyperspectral image compressor called BH which considers its input image as being partitioned into square blocks, each lying entirely within a particular band, and compresses one such block at a time by using the following steps: first predict the block from the corresponding block in the previous band, then select a predesigned code based on the prediction errors, and finally encode the predictor coefficient and errors. Apart from giving good compression rates and being fast, BH can provide random access to spatial locations in the image. We hypothesize that BH works well because it accommodates the rapidly changing image brightness that often occurs in hyperspectral images. We also propose an intra-band compressor called LM which is worse than BH, but whose performance helps explain BH's performance.