An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

We present an image classification system based on a Conditional Random Field (CRF) model trained on simple features obtained from a small number of semantically representative image patches. The CRFs are very powerful to handle complex parts dependencies due to their approach based on the effective

In order to improve the performance of Khmer named entity recognition(NER), a NER method based on conditional random field (CRF) model fusing with Khmer entity characteristics constraints is proposed in this paper. First of all, we carried out analyses on the Khmer entity characteristics, summarized the constraint on these entity characteristics and introduced into the CRF; then solved the labeling sequence by integer linear programming integrated entity characteristic constraint and obtained a model of CRF integrated with constraints based on Khmer entity characteristics. Based on a contrastive experiment, CRF model of the constraint has a better performance than traditional CRF model when carrying out the Khmer NER.

Occlusion reasoning is a fundamental problem in computer vision. In this paper, we propose an algorithm to recover the occlusion boundaries and depth ordering of free-standing structures in the scene. Rather than viewing the problem as one of pure image processing, our approach employs cues from an estimated surface layout and applies Gestalt grouping principles using a conditional random field (CRF) model. We propose a hierarchical segmentation process, based on agglomerative merging, that re-estimates boundary strength as the segmentation progresses. Our experiments on the Geometric Context dataset validate our choices for features, our iterative refinement of classifiers, and our CRF model. In experiments on the Berkeley Segmentation Dataset, PASCAL VOC 2008, and LabelMe, we also show that the trained algorithm generalizes to other datasets and can be used as an object boundary predictor with figure/ground labels.

In this paper an automatic method to implicitly model intonation for statistical parametric speech synthesis (SPSS) is presented. The approach is ideally suited to single speaker speech databases as used in text-to-speech (TTS), due to the models being speaker-specific. Fundamental frequency curves are automatically stylized based on the speaker-specific acoustics in the recorded database, requiring no models rooted in linguistic theory, and therefore being well suited to intonation modelling in under-resourced languages. The stylized curves are then coded into abstract pitch labels, which are used as features in the training of the statistical parametric acoustic models. A conditional random field (CRF) model is trained in order to predict the abstract pitch labels from the text for synthesis. The CRF model can be used to predict the abstract pitch labels on the syllable, word and phrase tiers. Objective and subjective results on synthetic voices built from English and isiXhosa speech databases are shown.

The amount of legislative documents produced within the past decade has risen dramatically, making it difficult for law practitioners to consult and update legislation. Named Entity Recognition (NER) systems have the untapped potential to extract information from legal documents, which can improve information retrieval and decision-making processes. We introduce the UlyssesNER-Br, a corpus of Brazilian Legislative Documents for NER with quality baselines. The presented corpus consists of bills and legislative consultations from Brazilian Chamber of Deputies. We implemented Conditional Random Field (CRF) and Hidden Markov Model (HMM) models, and the promising F1-score of 80.8% in the analysis by categories and 81.04% in the analysis by types, was achieved with the CRF model. The entities with the best average F1-score results were “FUNDlei” and “DATA”, and the ones with the worst results were “EVENTO” and “PESSOAgrupoind”. The corpus was also evaluated using a BiLSTM-CRF and Glove architectures provided by the pioneering state-of-the-art paper, achieving F1-score of 76.89% in the analysis by categories and 59.67% in the analysis by types.

The paper presented a novel discriminative model for efficient and effective recognition and simultaneous semantic segmentation of objects in images. The images are first segmented to give 'super-pixels'. Then the super-pixels are merged together and semantically labeled using a Condition Random Field (CRF) model. The use of a conditional random field allows us to incorporate different cues in a single unified model. The test on the standard dataset shows that compared with existing systems, the proposed system produces a detailed segmentation of a test image into coherent regions, with a semantic label associated with each region in the image.

In this paper we present a novel conditional random field (CRF) model based on Laplacian mixtures for image labeling. Nature images posses many spatial regularities that can be efficiently modeled by probabilistic graphical models such as CRF. Usually hundreds of features and several types of feature functions are used together which increases computational complexity and makes the training difficult to converge. We propose a new Laplacian mixture CRF model, which simplifies the training and inference process without losing labeling accuracy. The belief propagation inference and stochastic gradient descent training are formulated accordingly for the new model. The experimental results demonstrate that the new approach achieves better classification accuracy than the baseline CRF and comparable results with the state-of-the-art complex models.

The Conditional Random Fields (CRFs) only can deal with the sequence data of Markov property. And it cannot realize the relationship labeling with more fine structure between semantic roles. An approach to semantic role labeling (SRL) based on Chain Conditional Random Fields (CCRFs) Model was proposed. The long-distance dependencies between different state variants were handled effectively via labeling Hierarchical Dependencies and Brother Dependencies of syntactic dependency tree. Moreover, some new combinative features and prepositional phrase also were added though taking advantages of any features can be added in CRFs model. The experiments were implemented on CoNLL 2008 Shared Task. The results indicate the proposed method can improve precision and recall rate of the system.

Contour detection is a fundamental problem in computer vision. However, there is still a considerable disparity between detection results and actual contours. To detect object-level contours on the basis of comprehensive analysis of potential edges, we present a deep-learning-based approach with a conditional random fields (CRF) model. We obtain the initial edgemap with a VGGNet-based model, and establish a contour correlation graph (CCG) to describe the potential edges and their relationships. Then a CRF model is adopted to fulfill the optimization on the basis of the analysis of object-level contour characteristics and to predict the validity of candidate contours. The experiment results on various datasets demonstrate that the proposed method outperforms the existing methods by a noticeable margin.

The use of top-down categorization information in bottom-up semantic segmentation can significantly improve its performance. The basic Conditional Random Field (CR-F) model can capture the local contexture information, while the locality-consistent sparse representation can obtain the category-level priors and the relationship infeature space. In this paper, we propose a novel semantic segmentation method based on an innovative CRF with locality-consistent dictionary learning. The framework aims to model the local structure in both location and feature space as well as encourage the discrimination of dictionary. Moreover, an adapted algorithm for the proposed model is described. Extensive experimental results on Graz-02, PASCAL VOC 2010 and MSRC-21 databases demonstrate that our method is comparable to or outperforms state-of-the-art Bag-of-Features (BoF) based segmentation methods.

We present an approach for joint inference of 3D scene structure and semantic labeling for monocular video. Starting with monocular image stream, our framework produces a 3D volumetric semantic + occupancy map, which is much more useful than a series of 2D semantic label images or a sparse point cloud produced by traditional semantic segmentation and Structure from Motion(SfM) pipelines respectively. We derive a Conditional Random Field (CRF) model defined in the 3D space, that jointly infers the semantic category and occupancy for each voxel. Such a joint inference in the 3D CRF paves the way for more informed priors and constraints, which is otherwise not possible if solved separately in their traditional frameworks. We make use of class specific semantic cues that constrain the 3D structure in areas, where multiview constraints are weak. Our model comprises of higher order factors, which helps when the depth is unobservable.We also make use of class specific semantic cues to reduce either the degree of such higher order factors, or to approximately model them with unaries if possible. We demonstrate improved 3D structure and temporally consistent semantic segmentation for difficult, large scale, forward moving monocular image sequences.

Interactive image segmentation has gained much popularity since it can enhance the segmentation performance with human interactions. In this paper, we propose a novel framework that can effectively estimate the binary labels based on Conditional Random Field (CRF) models combining region-level information with pixels-level information. The user scribbles are treated as soft constraints. We use pixel-layer with local CRF and region-layer with fully connected CRF to model the graph. Then we combine the pixel information and region information together to calculate the pixels' likelihood term. In contrast to using relations between the pixel-layer and the region-layer during the probability computation, we calculate the probabilities of pixels and regions separately to reduce computational complexity. After the likelihood computation, a graph cut based model is used to refine the pixel-level label predictions of the image in order to avoid label inconsistency in regions and capture the boundary details. Due to the less relationship between layers and utilizing the refinement, the proposed model can provide high accuracy and user inputs insensitive results. Our experiments show that the proposed approach can efficiently and accurately segment the images compared with other state of art methods with only a few scribbles. Graph cut based fine tuning improves our segmentation accuracy and our method needs less computational cost than traditional multi-layer-based methods.

With the availability of low-cost depth-visual sensing devices, such as Microsoft Kinect, we are experiencing a growing interest in indoor environment understanding, at the core of which is semantic segmentation in RGB-D image. The latest research shows that the convolutional neural network (CNN) still dominates the image semantic segmentation field. However, down-sampling operated during the training process of CNNs leads to unclear segmentation boundaries and poor classification accuracy. To address this problem, in this paper, we propose a novel end-to-end deep architecture, termed FuseCRFNet, which seamlessly incorporates a fully-connected Conditional Random Fields (CRFs) model into a depth-based CNN framework. The proposed segmentation method uses the properties of pixel-to-pixel relationships to increase the accuracy of image semantic segmentation. More importantly, we formulate the CRF as one of the layers in FuseCRFNet to refine the coarse segmentation in the forward propagation, in meanwhile, it passes back the errors to facilitate the training. The performance of our FuseCRFNet is evaluated by experimenting with SUN RGB-D dataset, and the results show that the proposed algorithm is superior to existing semantic segmentation algorithms with an improvement in accuracy of at least 2%, further verifying the effectiveness of the algorithm.

Histopathological identification of tumor tissue is one of the routine pathological diagnoses for pathologists. Recently, computational pathology has been successfully interpreted by a variety of deep learning-based applications. Nevertheless, the high-efficient and spatial-correlated processing of individual patches have always attracted attention in whole-slide image (WSI) analysis. In this paper, we propose a high-throughput system to detect tumor regions in colorectal cancer histology slides precisely. We train a deep convolutional neural network (CNN) model and design a Monte Carlo (MC) adaptive sampling method to estimate the most representative patches in a WSI. Two conditional random field (CRF) models are designed, namely the correction CRF and the prediction CRF are integrated for spatial dependencies of patches. We use three datasets of colorectal cancer from The Cancer Genome Atlas (TCGA) to evaluate the performance of the system. The overall diagnostic time can be reduced from 56.7 percent to 71.7 percent on the slides of a varying tumor distribution, with an increase in classification accuracy.

In this paper, we propose a synthetic aperture radar (SAR) image change detection algorithm based on SAR image nonstationary analysis and conditional random field (CRF) model. This algorithm segments the SAR images into two nonstationary regions with the Triplet Markov Field (TMF) model which incorporates a U field to describe the images' nonstationary nature. The combined posterior probability of the CRF model consists of two parts: the unary potential and the pairwise potential. The unary potential is modeled by support vector machine (SVM) using the texture features extracted from the two-temporal SAR images, and the pairwise potential is constructed by the multilevel logistical model to capture the spatial interactions of the log-ratio image. Both the unary and pairwise potentials are improved with the introduction of the U field. The parameters of the pairwise potential are estimated by the iterative conditional estimation (ICE) algorithm. The iterative conditional model (ICM) algorithm is utilized for updating the combined posterior probability, and the final change detection results are obtained when it converges. Finally, experimental results on four two-temporal SAR images validate the effectiveness of the proposed image change detection algorithm.