Visual feature coding based on heterogeneous structure fusion for image classification

Lin, Guanghui; Fan, Caixia; Zhu, Hong; Miu, Yalin; Kang, Xiaobing

doi:10.1016/j.inffus.2016.12.010

Cited by 25 publications

(15 citation statements)

References 29 publications

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“…Lin et al . [ 49 ] proposed a local visual feature coding based on heterogeneous structure fusion to overcome the limitation of capturing intrinsic invariance in intra-class images or image structure for large variability image classification. Our methods provides 3.18% higher accuracy compared to their approach.…”

Section: Experimental Datasets and Resultsmentioning

confidence: 99%

A Hybrid Geometric Spatial Image Representation for scene classification

et al. 2018

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The recent development in the technology has increased the complexity of image contents and demand for image classification becomes more imperative. Digital images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and image representation is considered as an important step in scene analysis as it affects the image classification performance. Automatic classification of images is an open research problem for image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an image contains no information about the co-occurrence of features in the 2D image space. This is considered as a limitation, as the spatial arrangement among visual words in image space contains the information that is beneficial for image representation and learning of classification model. To deal with this, researchers have proposed different image representations. Among these, the division of image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the image. Five standard image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy.

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Section: Experimental Datasets and Resultsmentioning

confidence: 99%

A Hybrid Geometric Spatial Image Representation for scene classification

et al. 2018

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“…The second kind of method attempts to mine the nonlinear relationship of the heterogeneous feature structure based on the global feature [10,11] or the local feature encoding [12] for finding the complementary of the different structures in multiple representations. These methods can bridge the gap between heterogeneous structures for the uniform fusion feature representation.…”

Section: Structure Fusionmentioning

confidence: 99%

“…to discriminate the different data objects.The recent learning methods for mining multi-graph structure information mainly have tow categories. One is structure fusion [7][8][9][10][11][12][13][14][15][16][17] or diffusion on the tensor product graph [18-23] based on the complete data, which include each view observation datum. Another is graph convolutional networks for the salient graph structure preservation [6] or node information fusion [24,25] based on incomplete data, which lacks some view observation data.…”

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confidence: 99%

Structure Fusion Based on Graph Convolutional Networks for Node Classification in Citation Networks

et al. 2020

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Suffering from the multi-view data diversity and complexity, most of the existing graph convolutional networks focus on the networks’ architecture construction or the salient graph structure preservation for node classification in citation networks and usually ignore capturing the complete graph structure of nodes for enhancing classification performance. To mine the more complete distribution structure from multi-graph structures of multi-view data with the consideration of their specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of node classification in a semi-supervised way. SF-GCN can not only exploit the special characteristic of each view datum by spectral embedding preserving multi-graph structures, but also explore the common style of multi-view data by the distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures; we can construct the optimization function of the structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as the adjacent matrix to input graph convolutional networks for node classification in a semi-supervised way. Furthermore, we generalize the structure fusion to structure diffusion propagation and present structure propagation fusion based on graph convolutional networks (SPF-GCN) for utilizing these structure interactions. Experiments demonstrate that the performance of SPF-GCN outperforms that of the state-of-the-art methods on three challenging datasets, which are Cora, Citeseer, and Pubmed in citation networks.

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“…HVFC-HSF [30] 70.7% 78.7% CLGC(RGB-RGB) [31] -72.6% CSAE [24] 64.0% 71.4% Hybrid-CNN [25] -84.8% FScSPM (Our Approach) 76.3% 84.8%…”

Section: Algorithms 15 Training 30 Trainingmentioning

confidence: 99%

A Fast Sparse Coding Method for Image Classification

et al. 2019

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Image classification is an important problem in computer vision. The sparse coding spatial pyramid matching (ScSPM) framework is widely used in this field. However, the sparse coding cannot effectively handle very large training sets because of its high computational complexity, and ignoring the mutual dependence among local features results in highly variable sparse codes even for similar features. To overcome the shortcomings of previous sparse coding algorithm, we present an image classification method, which replaces the sparse dictionary with a stable dictionary learned via low computational complexity clustering, more specifically, a k-medoids cluster method optimized by k-means++. The proposed method can reduce the learning complexity and improve the feature’s stability. In the experiments, we compared the effectiveness of our method with the existing ScSPM method and its improved versions. We evaluated our approach on two diverse datasets: Caltech-101 and UIUC-Sports. The results show that our method can increase the accuracy of spatial pyramid matching, which suggests that our method is capable of improving performance of sparse coding features.

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Visual feature coding based on heterogeneous structure fusion for image classification

Cited by 25 publications

References 29 publications

A Hybrid Geometric Spatial Image Representation for scene classification

A Hybrid Geometric Spatial Image Representation for scene classification

Structure Fusion Based on Graph Convolutional Networks for Node Classification in Citation Networks

A Fast Sparse Coding Method for Image Classification

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