Finding similar images quicky using object shapes

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

Research on the Trust Governance in the Venture Capital Syndication: An Evolutionary Game Model

The traditional privacy-preserving image retrieval schemes not only bring large computational and communication overhead but also cannot well protect the image and query privacy in multi-user scenarios. To solve the above problems, we first propose a basic privacy-preserving content-based image retrieval (CBIR) framework which significantly reduces storage and communication overhead compared with the previous works. Furthermore, we design a new efficient key conversion protocol to support unshared key multi-owner multi-user image retrieval without losing search precision. Moreover, our framework supports unbounded attributes and can trace malicious users according to leaked secret keys, which significantly improve the usability of multi-source data sharing. Strict security analysis shows that the user privacy and outsourced data security can be guaranteed during the image retrieval process, and the performance analysis using real-world dataset shows that the proposed image retrieval framework is efficient and feasible for practical applications.

Soutien social, dépression et estime de soi chez les personnes âgées : les résultats d’une analyse en cluster

Validation of EN ISO method 10273 - Detection of pathogenic Yersinia enterocolitica in foods

Image retrieval is one of the most interesting and fastest growing research areas in all fields. It is an effective and efficient tool for managing large image databases. In most Content-Based Image Retrieval (CBIR) systems, an image is represented by a set of low-level visual features; hence a direct correlation with high-level semantic information will be absent. Therefore, a gap exists between high-level information and low-level features, which is the main reason that hinders the improvement of the image retrieval accuracy. In this work, main focus is on the semantic based image retrieval system using Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. Based on the texture features, semantic interpretations are given to the extracted textures. The images are retrieved according to user satisfaction and thereby reduce the semantic gap between low level features and high level features.

Text-based image retrieval is a popular and simple framework, which consists in using text annotations (e.g., image names, tags) to efficiently collect images relevant to a query word, from very large image collections. Even if the set of images retrieved using text annotations is noisy, it constitutes a reasonable initial set of images that can be considered as a bootstrap and improved further by analyzing image content. In this context, this paper introduces an approach for improving this initial set by re-ranking the so-obtained images, assuming that non-relevant images are scattered (i.e., they do not form clusters), unlike the relevant ones. More specifically, the approach consists in computing efficiently and on-the-fly closed frequent patterns, and in re-ranking images based on the number of patterns they contain. To do this, the paper introduces a simple but powerful new scoring function. Moreover, after the re-ranking process, we show how pattern mining techniques can also be applied for promoting diversity in the top-ranked images. The approach is validated on three different datasets for which state-of-the-art results are obtained.

Handwritten signature retrieval and identification

RGB-D sensor has gained its popularity in the study of object recognition for its low cost as well as its capability to provide synchronized RGB and depth images. Thus, researchers have proposed new methods to extract features from RGB-D data. On the other hand, learning-based feature representation is a promising approach for 2D image classification. By exploiting sparsity in 2D image signals, we can learn image representation instead of using hand-crafted local descriptors like SIFT or HoG. This framework inspired us to learn features from RGB-D data. Our work focuses on two goals. First, we propose a novel Hierarchical Sparse Shape Descriptor (HSSD) to form learning-based representation for 3D shapes. To achieve this, we analyze several 3D feature extraction techniques and propose a unified view of them. Then, we learn hierarchical shape representation with sparse coding, max pooling and local grouping. Second, we investigate whether RGB and depth information should be fused at lower level or higher level. Experimental results show that, first, our HSSD algorithm can learn shape dictionary and provide shape cues in addition to the 2D cues. Using the proposed HSSD algorithm achieves 84% accuracy on a household RGB-D object dataset and outperforms a widely used VFH shape feature by 13%. Second, fusing RGB-D information at lower level does not improve recognition performance.

To explore possible interrelationships among resistance to peer pressure, risky decision-making and health risk behaviors among young adolescents. Based on the cluster sampling method, the participants who were recruited from 5 junior middle schools in Guangzhou and 3 junior middle schools in Shenyang city on October, 2010, were administered to complete the questionnaire concerned with their experiences with drinking and smoking during the past 30 days preceding the survey, and the hours using computer daily both in weekdays and in weekend. The level of resistance to peer influence and risky decision-making were assessed by Resistance to peer influence scale (RPIS) and Youth decision-making questionnaire (YDMQ). Logistic regression was used to explore possible interrelationships among resistance to peer influence, risky decision-making and health risk behaviors among young adolescents. A total of 1985 questionnaires were valid, including 1001(50.4%) boys and 984 (49.6%) girls. About 27.1% (537/1985) junior middle school students reported having health risk behaviors, boys' (30.7%, 307/1001) was higher than girls' (23.4%, 230/984) with significant gender difference (P < 0.05). The prevalence of smoking, drinking during the past 30 days before the survey and using computer over 3 hours daily in weekdays and in weekend were 5.1% (102/1985), 14.3% (284/1985), 3.5% (70/1985) and 13.7% (272/1985), respectively. The rate of drinking, using computer over 3 hours daily in weekdays and in weekend were higher in males (16.4% (164/1001), 4.5% (45/1001), 16.2% (162/1001)) than those in females (12.2% (120/984), 2.5% (25/984), 11.2% (110/984)) (P < 0.05). The scores of RPIS and YDMQ of the two cities adolescents were 2.82 ± 0.39 and 1.68 ± 0.62. The students reported smoking, drinking during the past 30 days before the survey and using computer over 3 hours daily in weekend gained lower RPIS scores (2.43 ± 0.40, 2.61 ± 0.41, 2.77 ± 0.40) than their counterparts who didn't report these kind of health risk behaviors (2.84 ± 0.38, 2.85 ± 0.38, 2.82 ± 0.39)(P < 0.05). And those reported smoking, drinking during the past 30 days before the survey and using computer over 3 hours daily in weekdays and in weekend gained higher YDMQ scores (2.38 ± 0.66, 2.06 ± 0.66, 1.97 ± 0.72, 1.84 ± 0.64, respectively) than their counterparts who didn't report these kind of health risk behaviors (1.64 ± 0.38, 1.61 ± 0.58, 1.67 ± 0.61, 1.65 ± 0.61, respectively) (P < 0.05). After adjusting gender, area, parental education degree, self-reported family economic condition, multi-variant logistic regression analysis indicated that the low and middle level of resistance to peer influence (low and middle level vs high level, had odds ratios of 2.97 (1.96 - 4.50) and 1.51 (1.05 - 2.16)), and also the middle and high level of risky decision-making (middle and high level vs low level, had odds ratios of 1.62 (1.19 - 2.22) and 3.43 (2.39 - 4.90)) were all the risk factors of adolescent health risk behaviors. Adolescents with poor ability of resistance to peer pressure and high risky decision-making were both the risk factors of adolescent health risk behaviors.

In this paper, we present a grid-based framework for image retrieval. In order to represent the intricate composition of images, the grid-based approach partitions each image into blocks from which a feature representation is derived from the local low-level content. Since the background often dominates the subject in the foreground, a special query selection method was developed. It combines the salient region-of-interest/query-by-example paradigm with coarse segmentation to remove the irrelevant background regions. The proposed search method looks for similar features across all block positions and at several scales. Existing local grid-based methods are constrained by searching for objects in the same position as the query object. Using this framework, the spatial constraint can be eliminated, and steps toward scale invariance can be taken. Promising results show that the grid-based method performs better than global search.

Learning in an image retrieval scheme that uses hidden annotations is investigated. Compared with low level visual features that are straightforward functions of the raw pixel values, hidden annotations are higher level hidden semantic attributes. In the proposed scheme, a small set of images is manually labeled with several hidden annotations. For each annotation, a support vector machine (SVM) classifier is trained using the images labeled with it as positive examples and others as negative examples. Based on the trained SVMs, the annotations are propagated to the unlabeled images in the database. To perform relevance feedback in the annotation space, a probabilistic re-weighting algorithm is proposed. Experimental results on a general-purpose database of 10,000 images demonstrate the potential of hidden annotation-based image retrieval and the superiority of the proposed relevance feedback algorithm over two existing algorithms.

The phenomenon, that the flow rate of a loop with a cross pipe switches nonperiodically between a high level and a low level under certain flow conditions was found and experimentally investigated by using air and water. The flow behaves as if a bistable flow state exists like a flow in fluidics. It is observed by a flow visualization technique (spark tracing method and air injection method) that the switching synchronizes with the generation and the collapse of a swirl flow through opposing laterals of the cross pipe. The pressure loss coefficient of branching at the low flow rate level is about twice as much as that at the high flow rate level.

Viscosity negatively affects the nutritional value of blue lupin seeds for broilers

In multilevel modeling, one often distinguishes between macro-micro and micro-macro situations. In a macro-micro multilevel situation, a dependent variable measured at the lower level is predicted or explained by variables measured at that lower or a higher level. In a micro-macro multilevel situation, a dependent variable defined at the higher group level is predicted or explained on the basis of independent variables measured at the lower individual level. Up until now, multilevel methodology has mainly focused on macro-micro multilevel situations. In this article, a latent variable model is proposed for analyzing data from micro-macro situations. It is shown that regression analyses carried out at the aggregated level result in biased parameter estimates. A method that uses the best linear unbiased predictors of the group means is shown to yield unbiased estimates of the parameters.