13763246
Semi-automatic dynamic auxiliary-tag-aided image annotation
Image annotation is the foundation for many real-world applications. In the age of Web 2.0, image search and browsing are largely based on the tags of images. In this paper, we formulate image annotation as a multi-label learning problem, and develop a semi-automatic image annotation system. The presented system chooses proper words from a vocabulary as tags for a given image, and refines the tags with the help of the user's feedback. The refinement amounts to a novel multi-label learning framework, named Semi-Automatic Dynamic Auxiliary-Tag-Aided (SADATA), in which the classification result for one certain tag (target tag) can be boosted by the classification results of a subset of the other tags (auxiliary tags). The auxiliary tags, which have strong correlations with the target tag, are determined in terms of the normalized mutual information. We only select those tags whose correlations exceed a threshold as the auxiliary tags, so the auxiliary set is sparse. How much an auxiliary tag can contribute is dependent on the image, so we also build a probabilistic model conditioned on the auxiliary tag and the input image to adjust the weight of the auxiliary tag dynamically. For an given image, the user feedback on the tags corrects the outputs of the auxiliary classifiers and SADATA will recommend more proper tags next round. SADATA is evaluated on a large collection of Corel images. The experimental results validate the effectiveness of our dynamic auxiliary-tag-aided method. Furthermore, the performance also benefits from user feedbacks such that the annotation procedure can be significantly speeded up.
13758087
Support cluster machine
For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.
17395966
Scene segmentation based on video structure and spectral methods
Scene is an important semantic unit for video analysis, retrieval and browsing. However, due to the lack of a generic algorithm, many studies focus on specific methods for certain video genes, e.g., news, sports, etc. In this paper, we propose a general framework for scene segmentation. First, we construct a graph, in which the elements encode the shot-to-shot coherent characteristics of a video clip based on visual similarity and temporal relation between shots. In this step, we only exploit the inherent property of video itself and it is independent of video genres. Second, spectral method is applied on the graph to group shots into scenes. The proposed method is not only simple but also effective to deal with organized features. Experimental results validate the robustness of our method on different kinds of videos.
5644766
Transfer learning for collaborative filtering via a rating-matrix generative model
Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately.
672476
Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction
The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring useritem rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods.
7632071
Position-sensing based a new docking system of RPRS
Docking is an essential function for reconfigurable planetary robot system (RPRS), it supports almost all metamorphic characteristics of the system. This paper presents a space docking method for self-reconfigurable modular robot system. The method adopts a position sensory detector (PSD) to get the space geometrical relation between modular child robots. By using the kinematics and inverse kinematics modeling of the child robot, a docking control algorithm is developed to achieve space dock automatically. To validate the method, simulation experiments were done on a platform based on OPENGL, and the simulating results were given as well.
13920180
Influence of Incomplete Linkage between Marked and Functional Gene on Estimating Gene Effect by Molecular Marker Regression Model
In the past decades, regression approach was a main gene mapping method. For avoiding the QTL mapping to estimate the gene effects, the molecular marker regression was used to research the quantitative trait controlled by polygene under the condition of complete linkage between gene locus and marker. For the utilization of this method, the recombination influence on precision and accuracy estimating gene effect were researched. The results showed the recombination rate between a marker and its locus could affect the precision and accuracy estimating gene effect and allele’s interaction effects. The precision and accuracy were reduced along with the increasing of the recombination rate between a marker and its locus, the larger the recombination rate was, the smaller the precision and accuracy were. The precision and accuracy estimating for one gene effect and allele’s interaction effects could only be affected by its own recombination rate between its own marker and its locus, not by other genes recombination rate. These suggested that it was need to find a marker which was tightly or closely linked with genes for estimating the gene effect and allele’s interaction effects precisely, especially for a gene locus with a large allele’s interaction coefficient and relative gene effects.
9214943
A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution
A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.
38238585
Exploiting multi-scale support vector regression for image compression
Unlike traditional neural networks that require predefined topology of the network, support vector regression (SVR) approach can model the data within the given level of accuracy with only a small subset of the training data, which are called support vectors (SVs). This property of sparsity has been exploited as the basis for image compression. In this paper, for still image compression, we propose a multi-scale support vector regression (MS-SVR) approach, which can model the images with steep variations and smooth variations very well resulting in good performance. We test our proposed MS-SVR based algorithm on some standard images. The experimental results verify that the proposed MS-SVR achieves better performance than standard SVR. And in a wide range of compression ratio, MS-SVR is very close to JPEG in terms of peak signal-to-noise ratio (PSNR) but exhibits better subjective quality. Furthermore, MS-SVR even outperforms JPEG on both PSNR and subjective quality when the compression ratio is higher enough, for example 25:1 for Lena image. Even when compared with JPEG-2000, the results show greatly similar trend as those in JPEG experiments, except that the compression ratio is a bit higher where our proposed MS-SVR will outperform JPEG-2000.
13209883
An Approach for Dynamic Hardware /Software Partitioning Based on DPBIL
HW/SW partitioning is one of the key steps in high-level modeling of embedded system co-design. In some kinds of data-dependence applications, the partitioning environment is dynamic. An approach based on a kind of evolutionary algorithm called DPBIL is proposed for dynamic HW/SW partitioning problem. DPBIL is inspired by Dualism or complementarity in the nature and operates on a pair of dual probability vectors to adapt itself to the dynamic environment. We systematically constructed the system model, defined the dynamic environment and conducted experimental analysis via comparison with the existing method. To enhance the exploration performance of DPBIL, a threshold constraint mechanism is introduced. Experimental results indicated that our approach is feasible and efficient for dynamic HW/SW partitioning, and has much better performance than traditional ones.