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Lizhe Wang

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6 papers

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EAAI Journal 2024 Journal Article

MFFSP: Multi-scale feature fusion scene parsing network for landslides detection based on high-resolution satellite images

Penglei Li
Yi Wang
Tongzhen Si
Kashif Ullah
Wei Han
Lizhe Wang

Fast and efficient landslide detection plays an important role in post-disaster rescue and risk assessment. Existing convolution neural network (CNN) based landslide detection methods are difficult to exploit global long-distance dependencies due to limited receptive fields. Considering that landslide occurrence is susceptible to local and global conditions, we propose a novel multi-scale feature fusion scene parsing (MFFSP) framework to explore information at different scales by coupling CNN with Transformer to learn local and global clues for landslide detection based on satellite data. In the encoder, we design three modules, visual geometry module (VGM), residual learning module (RLM), and Transformer module (TRM) to exploit multi-scale features. Specifically, VGM and RLM are constructed based on convolution operations to explore local features by learning low-level and middle-level information, while TRM is built based on self-attention mechanism to learn long-distance dependencies. In the decoder, TRM and VGM are further extended to motivate the model to mine long-distance dependencies and detailed spatial information by deeply fusing features from multiple scales. To demonstrate the performance of the model, we employ two study areas with four test regions to conduct experiments and compare with seven state-of-the-art deep learning models. Extensive experiments demonstrate that MFFSP greatly outperforms other algorithms. In addition, we conduct numerous ablation experiments, proving that MFFSP fully combines the complementary advantages of CNN and Transformer to mine robust features.

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IJCAI Conference 2021 Conference Paper

Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

Chang Tang
Xinwang Liu
En Zhu
Lizhe Wang
Albert Zomaya

In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.

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AAAI Conference 2020 Conference Paper

CGD: Multi-View Clustering via Cross-View Graph Diffusion

Chang Tang
Xinwang Liu
Xinzhong Zhu
En Zhu
Zhigang Luo
Lizhe Wang
Wen Gao

Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views. Though achieving great success in various applications, we observe that most of previous methods learn a consensus graph by building certain data representation models, which at least bears the following drawbacks. First, their clustering performance highly depends on the data representation capability of the model. Second, solving these resultant optimization models usually results in high computational complexity. Third, there are often some hyperparameters in these models need to tune for obtaining the optimal results. In this work, we propose a general, effective and parameter-free method with convergence guarantee to learn a uniﬁed graph for multi-view data clustering via cross-view graph diffusion (CGD), which is the ﬁrst attempt to employ diffusion process for multi-view clustering. The proposed CGD takes the traditional predeﬁned graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by 1) capturing the underlying manifold geometry structure of original data points, and 2) leveraging the complementary information among multiple graphs. The ﬁnal uniﬁed graph used for clustering is obtained by averaging the improved view associated graphs. Extensive experiments on several benchmark datasets are conducted to demonstrate the effectiveness of the proposed method in terms of seven clustering evaluation metrics.

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AAAI Conference 2020 Conference Paper

R²MRF: Defocus Blur Detection via Recurrently Refining Multi-Scale Residual Features

Chang Tang
Xinwang Liu
Xinzhong Zhu
En Zhu
Kun Sun
Pichao Wang
Lizhe Wang
Albert Zomaya

Defocus blur detection aims to separate the in-focus and out-of-focus regions in an image. Although attracting more and more attention due to its remarkable potential applications, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales and missing boundary details of defocus blur regions. In order to address these issues, we propose a deep neural network which Recurrently Refines Multi-scale Residual Features (R2MRF) for defocus blur detection. We firstly extract multi-scale deep features by utilizing a fully convolutional network. For each layer, we design a novel recurrent residual refinement branch embedded with multiple residual refinement modules (RRMs) to more accurately detect blur regions from the input image. Considering that the features from bottom layers are able to capture rich low-level features for details preservation while the features from top layers are capable of characterizing the semantic information for locating blur regions, we aggregate the deep features from different layers to learn the residual between the intermediate prediction and the ground truth for each recurrent step in each residual refinement branch. Since the defocus degree is sensitive to image scales, we finally fuse the side output of each branch to obtain the final blur detection map. We evaluate the proposed network on two commonly used defocus blur detection benchmark datasets by comparing it with other 11 state-of-the-art methods. Extensive experimental results with ablation studies demonstrate that R2MRF consistently and significantly outperforms the competitors in terms of both efficiency and accuracy.

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AAAI Conference 2019 Conference Paper

Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection

Chang Tang
Xinzhong Zhu
Xinwang Liu
Lizhe Wang

Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2, 1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.

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KER Journal 2015 Journal Article

A survey on text mining in social networks

Rizwana Irfan
Christine K. King
Daniel Grages
Sam Ewen
Samee U. Khan
Sajjad A. Madani
Joanna Kolodziej
Lizhe Wang

Abstract In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

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