In this paper, a structured max-margin learning algorithm is developed to achieve more effective training of large number inter-related classifiers for multilabel image annotation application. To leverage images classifier training, each partitioned into set instances (image regions or patches) and an automatic instance label identification assign multiple labels (which are given at the level) most relevant instances. A K-way min-max cut clustering kernel weight determination, where base...

The local binary pattern (LBP) model is a simple and effective method of texture classification, but it sensitive to rotational noisy images. Although many variants LBP are proposed by scholars, there still several urgent problems, such as poor noise rotation immunity. In this paper, we propose robust descriptor, jumping refined (JRLP) for classification. particular, first extract difference count (JLDCP) consisting second-order diagonal represent the information in domain. To capture detail...

An image texture was defined in terms of pixel intensities and directionality. However, most the current representation methods did not consider two key factors simultaneously. To effectively capture directional intensity information texture, this paper, we propose a novel robust local descriptor, named locally extremal pattern (LDEP), for classification. It extracts difference count (DLDCP) being made up DLDCP odd positions even to express area first place. Furthermore, acquire extremum...

A feature selection method based on mutual information and support vector machine (SVM) is proposed in order to eliminate redundant improve classification accuracy. First, local correlation between features overall calculated by information. The reflects the inclusion relationship features, so are evaluated eliminated with analyzing correlation. Subsequently, concept of mean impact value (MIV) defined influence degree input variables output for SVM network MIV calculated. importance weights...

In this paper, a novel framework is developed to achieve effective summarization of large-scale image collection by treating the problem automatic as dictionary learning for sparse representation, e.g., task can be treated (i.e., given set reconstructed sparsely with dictionary). For specific category or mixture multiple categories, we have built sparsity model reconstruct all its images using subset most representative summary); and adopted simulated annealing algorithm learn such...

Aiming to implement image segmentation precisely and efficiently, we exploit new ways encode images achieve the optimal thresholding on quantum state space. Firstly, vector density matrix are adopted for representation of pixel intensities their probability distribution, respectively. Then, method based global entropy maximization (GQEM) is proposed, which has an equivalent object function Otsu’s, but gives a more explicit physical interpretation in language mechanics. To reduce time...

To support more effective searches in large-scale weakly-tagged image collections, we have developed a novel algorithm to integrate both the visual similarity contexts between images and semantic their tags for topic network generation word sense disambiguation. First, is generated characterize topics sufficiently. By organizing large numbers of according cross-modal inter-topic contexts, our can make semantics behind tag space explicit, so that users gain deep insights rapidly formulate...

In terms of Internet Things (IOT) system with the possibility criterion fuzziness and randomness security risk, we qualitatively analyze risk level IOT scene by describing generalization metrics potential impact likelihood occurrence every major threat scenarios. On this basis, proposed self-assessment algorithm adopting three-dimensional normal cloud model integrated consideration indicators, researching multi-rule mapping relationship between qualitative input safety indicators...

Research of graffiti character recognition and retrieval, as a branch traditional optical (OCR), has started to gain attention in recent years. We have investigated the special challenge image retrieval problem propose series novel techniques overcome challenges. The proposed bounding box framework locates components images construct meaningful strings conduct image-wise semantic-wise on rather than entire image. Using real world data provided by law enforcement community Pacific Northwest...

In this paper, we present a novel framework to achieve effective summarization of large-scale web images by treating the problem automatic image as dictionary learning for sparse coding, e.g., summary given set can be treated representation (i.e., set). For semantic category certain object class or concept), build sparsity model reconstruct all its relevant using subset most representative summary); and stepwise basis selection algorithm is developed learn such summary) minimizing an...

It is well accepted that using high-dimensional multi-modal visual features for image content representation and classifier training may achieve more sufficient characterization of the diverse properties images further result in higher discrimination power classifiers. However, classifiers a feature space requires large number labeled images, which will problem curse dimensionality. To tackle this problem, hierarchical subset selection algorithm proposed to enable accurate classification,...

In this paper, we propose two kinds of feature extracting frameworks that can extract cascaded class-specific and class-mixture features, respectively, by taking the restricted Boltzmann machine (RBM) as basic building blocks; further call them a CS-RBM CM-RBM extractor. The discriminations features from both are verified better than class-independent (traditional) RBM (CI-RBM) As one mini-batch samples randomly selected all classes during training phase traditional RBM, which make above...

Active contour model (ACM) has widely used for segmenting two-phase images. However, its performance may not be satisfactory some color texture images when their features cannot effectively extracted. To alleviate this problem, in paper, a novel neutrosophic set transformation matrix factorization-based active (NSTMF-AC) approach is proposed segmentation. The NSTMF-AC an effective and robust segmentation method. Particularly, to capture wide range of information, the method extracts from...

In this paper, we have developed a new multi-label multi-task learning framework to leverage large-scale weakly-tagged images for inter-related classifier training. A novel image and tag cleansing algorithm is tackling the issues of spam, synonymous, loose ambiguous tags obtain more relevant images. The visual concept network generated characterize inter-concept similarity contexts precisely determine tasks automatically. Through paradigm, our structured max-margin can both learn large...

Imaging by the remote sensing camera with a wide field of view (WFV) is great significance in issue polar environment. However, instability image motion velocity poses huge challenge to observation task since direction earth's rotation keeps changing regions. The edge blur WFV usually ignored. Therefore, specific theoretical model that images over region supposed describe distinctly. Mathematical expressions are obtained novel method projection and coordinate transformation. quantitative...