Label Propagation Approach Research Articles

Seeing the invisible in differential interference contrast microscopy images.

Automated microscopy image restoration, especially in Differential Interference Contrast (DIC) imaging modality, has attracted increasing attentions since it greatly facilitates long-term living cell analysis without staining. Although the previous work on DIC image restoration is able to restore the nuclei regions of living cells, it is still challenging to reconstruct the unnoticeable cytoplasm details in DIC images. In this paper, we propose to extract the tiny movement information of living cells in DIC images and reveal the hidden details in DIC images by magnifying the cells' motion as well as attenuating the intensity variation from the background. From our restored images, we can clearly observe the previously-invisible details in DIC images. Experiments on two DIC image datasets show that the motion-based restoration method can reveal the hidden details of living cells. In addition, we demonstrate our restoration method can also be applied to other imaging modalities such as the phase contrast microscopy to enhance cells' details. Furthermore, based on the pixel-level restoration results, we can obtain the object-level segmentation by leveraging a label propagation approach, providing promising results on facilitating the cell shape and behavior analysis. The proposed algorithm can be a software module to enhance the visualization capability of microscopes.

Label propagation based semi-supervised learning for software defect prediction

Software defect prediction can automatically predict defect-prone software modules for efficient software test in software engineering. When the previous defect labels of modules are limited, predicting the defect-prone modules becomes a challenging problem. In static software defect prediction, there exist the similarity among software modules, a software module can be approximated by a sparse representation of the other part of the software modules, and class-imbalance problem, the number of defect-free modules is much larger than that of defective ones. In this paper, we propose to use graph based semi-supervised learning technique to predict software defect. By using Laplacian score sampling strategy for the labeled defect-free modules, we construct a class-balance labeled training dataset firstly. And then, we use a nonnegative sparse algorithm to compute the nonnegative sparse weights of a relationship graph which serve as clustering indicators. Lastly, on the nonnegative sparse graph, we use a label propagation algorithm to iteratively predict the labels of unlabeled software modules. We thus propose a nonnegative sparse graph based label propagation approach for software defect classification and prediction, which uses not only few labeled data but also abundant unlabeled ones to improve the generalization capability. We vary the size of labeled software modules from 10 to 30 % of all the datasets in the widely used NASA projects. Experimental results show that the NSGLP outperforms several representative state-of-the-art semi-supervised software defect prediction methods, and it can fully exploit the characteristics of static code metrics and improve the generalization capability of the software defect prediction model.

A label propagation method using spatial-spectral consistency for hyperspectral image classification

ABSTRACTIn this article, a label propagation approach with automatic seed selection is developed for hyperspectral image classification. The core idea is to combine pixel-wise classification results with spatial information described by a data graph. Using only the support vector machine (SVM) classifier on spectral features to tackle the hyperspectral classification task will produce results with a salt-and-pepper appearance. To overcome this limitation, the spatial information is incorporated by label propagation. The performance of label propagation is dependent on two points: the seeds and the connection graph. Generally, a limited number of labelled samples are available, which are considered as seeds in label propagation. However, the limited seeds will result in bad label propagation. Therefore, pseudo-seeds are automatically selected in local windows. Specifically, the pixels whose initial labels according to SVM are consistent with their most spatial neighbours are selected as seeds. Through seed selection, the number of seeds is greatly increased. Then, the label information of the selected seeds is propagated to their spatial neighbours using a data graph which is constructed according to the local structures in the image. Through seed selection and label propagation on the graph, the problem of salt-and-pepper appearance is solved elegantly – the noisy labels are highly suppressed and most of the structures are preserved. Competitive experimental results on a variety of hyperspectral data sets demonstrate the effectiveness of the proposed method.

Can the Virtual Labels Obtained by Traditional LP Approaches Be Well Encoded in WLR?

Semisupervised dimension reduction via virtual label regression first derives the virtual labels of unlabeled data by employing a newly designed label propagation (LP) approach (called Special random walk (SRW)) and then encodes them in a weighted linear regression model. Nie et al. (2011) highlighted two important characteristics of SRW nonexistent in the previous LP approaches: outlier detection and probability value output, which guarantee the elegant encoding of the resultant virtual labels in the weighted label regression. However, in this brief, we show that the relationship between the SRW and the previous work on LP is very close. Naturally, a problem deserving investigation is whether traditional LP approaches are indeed unable to share the above two characteristics of SRW. We aim to address this problem.

A diversity-based search approach to support annotation of a large fish image dataset

Label propagation consists in annotating an unlabeled dataset starting from a set of labeled items. However, most current methods exploit only image similarity between labeled and unlabeled images in order to find propagation candidates, which may result, especially in very large datasets, in retrieving mostly near-duplicate images. While such approaches are technically correct, as they maximize the propagation precision, the resulting annotated dataset may not be as useful, since they lack intra-class variability within the set of images sharing the same label. In this paper, we propose an approach for label propagation which favors the propagation of an object's label to a set of images representing as many different views of that object as possible, while at the same time preserving the relevance of the retrieved items to the query. Our method is based on a diversity-based clustering technique using a random forest framework and a label propagation approach which is able to effectively and efficiently propagate annotations using a similarity-based approach operating on clusters. The method was tested on a very large dataset of fish images achieving good performance in automated label propagation, ensuring diversification of the annotated items while preserving precision.

Label propagation based on collaborative representation for face recognition

Recently, collaborative representation (CR) has been shown to produce impressive performance on face recognition. However, the performances of CR depend on the number of labeled training samples for each class. When the labeled training samples per class are insufficient, CR would perform inaccurately and correspondingly degrades the final recognition performance. To solve this problem, in this paper, we introduce the CR into semi-supervised learning and propose a novel semi-supervised label propagation approach based on collaborative representation. Based on the subspace assumption that samples of the same class lie in the same subspace, each labeled sample can be well represented by the unlabeled samples of the same class. Our algorithm exploits a large amount of unlabeled samples which contain much more useful information as a dictionary to represent labeled samples, and propagates the label information from labeled data to unlabeled data. Thus, the information of unlabeled data can be effectively explored in our method, which can further improve the performance of collaborative representation with limited labeled training samples. Furthermore, we introduce our label propagation into other semi-supervised learning algorithm to further improve its, recognition performance. Experimental results are presented to demonstrate the efficacy of the proposed method.

Enhancing semi-supervised learning through label-aware base kernels

Currently, a large family of kernel design methods for semi-supervised learning (SSL) problems builds the kernel by weighted averaging of predefined base kernels (i.e., those spanned by kernel eigenvectors). Optimization of the base kernel weights has been studied extensively in the literature. However, little attention was devoted to designing high-quality base kernels. The eigenvectors of the kernel matrix, which are computed irrespective of class labels, may not always reveal useful structures of the target. As a result, the generalization performance can be poor however hard the base kernel weighting is tuned. On the other hand, there are many SSL algorithms whose focus are not on kernel design but on the estimation of the class labels directly. Motivated by the label propagation approach, in this paper we propose to construct novel kernel eigenvectors by injecting the class label information under the framework of eigenfunction extrapolation. A set of “label-aware” base kernels can be obtained with greatly improved quality, which leads to higher target alignment and henceforth better performance. Our approach is computationally efficient, and demonstrates encouraging performance in semi-supervised classification and regression tasks.

Cross-lingual Sentiment Lexicon Learning With Bilingual Word Graph Label Propagation

In this article we address the task of cross-lingual sentiment lexicon learning, which aims to automatically generate sentiment lexicons for the target languages with available English sentiment lexicons. We formalize the task as a learning problem on a bilingual word graph, in which the intra-language relations among the words in the same language and the inter-language relations among the words between different languages are properly represented. With the words in the English sentiment lexicon as seeds, we propose a bilingual word graph label propagation approach to induce sentiment polarities of the unlabeled words in the target language. Particularly, we show that both synonym and antonym word relations can be used to build the intra-language relation, and that the word alignment information derived from bilingual parallel sentences can be effectively leveraged to build the inter-language relation. The evaluation of Chinese sentiment lexicon learning shows that the proposed approach outperforms existing approaches in both precision and recall. Experiments conducted on the NTCIR data set further demonstrate the effectiveness of the learned sentiment lexicon in sentence-level sentiment classification.

Distributed community detection in dynamic graphs

Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied Planted Bisection Model dyn-G(n,p,q) where the node set [n] of the network is partitioned into two unknown communities and, at every time step, each possible edge (u,v) is active with probability p if both nodes belong to the same community, while it is active with probability q (with q≪p) otherwise. We also consider a time-Markovian generalization of this model.We propose a distributed protocol based on the popular Label-Propagation approach and prove that, when the ratio p/q is larger than nb (for an arbitrarily small constant b>0), the protocol finds the right “planted” partition in O(log⁡n) time even when the snapshots of the dynamic graph are sparse and disconnected (i.e., when p=Θ(1/n)).

Nonparametric label propagation using mutual local similarity in nearest neighbors

The shift from model-based approaches to data-driven ones is opening new frontiers in computer vision. Several tasks which required the development of sophisticated parametric models can now be solved through simple algorithms, by offloading the complexity of the task to the amount of available data. However, in order to develop data-driven approaches, it is necessary to have large annotated datasets. Unfortunately, manual labeling of large scale datasets is a complex, error prone and tedious task, especially when dealing with noisy images or with fine-grained visual tasks.In this paper we present an automatic label propagation approach that transfers labels from a small set of manually labeled images to a large set of unlabeled items by means of nearest-neighbor search operating on HoG image descriptors. In particular, we introduce the concept of mutual local similarity between the labeled query image and its nearest neighbors as the condition to be verified for propagating labels.The performance evaluation, carried out on the COREL 5K dataset and on a dataset of 20 million underwater low-quality images, showed how big data combined to simple nonparametric approaches allows to solve effectively complex visual tasks.

Graph-based multimodal semi-supervised image classification

We investigate an image classification task where training images come along with tags, but only a subset being labeled, and the goal is to predict the class label of test images without tags. This task is important for image search engine on photo sharing websites. In previous studies, it is handled by first training a multiple kernel learning classifier using both image content and tags to score unlabeled training images and then establishing a least-squares regression (LSR) model on visual features to predict the label of test images. Nevertheless, there remain three important issues in the task: (1) image tags on photo sharing websites tend to be imperfect, and thus it is beneficial to refine them for final image classification; (2) since supervised learning with a subset of labeled samples may be unreliable in practice, we adopt a graph-based label propagation approach by extra consideration of unlabeled data, and also an approach to combining multiple graphs is proposed; (3) kernel method is a powerful tool in the literature, but LSR simply treats the visual kernel matrix as an image feature matrix and does not consider the powerful kernel method. By considering these three issues holistically, we propose a graph-based multimodal semi-supervised image classification (GraMSIC) framework to handle the aforementioned task. Extensive experiments conducted on three publicly available datasets show the superior performance of the proposed framework.

Label propagation with α-degree neighborhood impact for network community detection.

Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach with α-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of its α-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on the α-degree neighborhood impact of all the nodes. The α-degree neighborhood impact is also taken as the updating weight value, where the parameter impact scope α can be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods.

Geodesic patch-based segmentation.

Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results.

Towards Multi Label Text Classification through Label Propagation

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text classification paradigms cannot efficiently classify such multifaceted text corpus. Through our paper we are proposing a novel label propagation approach based on semi supervised learning for Multi Label Text Classification. Our proposed approach models the relationship between class labels and also effectively represents input text documents. We are using semi supervised learning technique for effective utilization of labeled and unlabeled data for classification .Our proposed approach promises better classification accuracy and handling of complexity and elaborated on the basis of standard datasets such as Enron, Slashdot and Bibtex.

Detecting network communities by propagating labels under constraints

We investigate the recently proposed label-propagation algorithm (LPA) for identifying network communities. We reformulate the LPA as an equivalent optimization problem, giving an objective function whose maxima correspond to community solutions. By considering properties of the objective function, we identify conceptual and practical drawbacks of the label-propagation approach, most importantly the disparity between increasing the value of the objective function and improving the quality of communities found. To address the drawbacks, we modify the objective function in the optimization problem, producing a variety of algorithms that propagate labels subject to constraints; of particular interest is a variant that maximizes the modularity measure of community quality. Performance properties and implementation details of the proposed algorithms are discussed. Bipartite as well as unipartite networks are considered.