Graph-based bag-of-words for classification

Silva, Fernanda B.; Werneck, Rafael de Oliveira; Goldenstein, Siome; Tabbone, Salvatore; Torres, Ricardo da Silva

doi:10.1016/j.patcog.2017.09.018

Cited by 52 publications

(28 citation statements)

References 47 publications

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“…In [2], the authors propose additional BoVW models: bag of graph words (BoGW), bag of singleton graphs (BoSG) and bag of visual graphs (BoVG), for digital object representation compliant to different applications. These models, which map images in graph space, are evaluated on IAM repository datasets and report great accuracy and execution time.…”

Section: Related Workmentioning

confidence: 99%

Bag of ARSRG Words (BoAW)

Manzo

Pellino²

2019

MAKE

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In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors.

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Section: Related Workmentioning

confidence: 99%

Bag of ARSRG Words (BoAW)

Manzo

Pellino²

2019

MAKE

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“…The squeeze operation is used to generate the channel-wise statistics by using the global average pooling operation after the convolution layer, so that the feature maps ( H W C ) become the real number series of11C  . z is defined as the output of squeeze operation, and the th c element of z is given by ( ) 11 1 , (15) where u denotes the output of the convolution layer. The convolution layer can be viewed as the collection of local descriptors, and the global average pooling layer makes the descriptors have a global receptive field.…”

Section: Squeeze and Excitation Blockmentioning

confidence: 99%

“…To improve the performance of SAR remote sensing scene classification, we should globally order the spatial structure in small regions, and the spatial structure should be orderless in large regions (due to the layout differences of the scene image); however, the deep feature representation lacks the orderless feature descriptor [14]. The bag of visual words (BoVW) model [15][16][17] which produces orderless features and makes features characterize intra-class differences [18] achieves better performance on the scene classification. The famous feature coding used in the BoVW pipeline includes vector quantization (VQ) [19,20], sparse coding (SC) [21], locality-constrained linear coding (LLC) [22], super vector (SV) [23], Fisher vector (FV) [24], locality-constrained affine subspace coding (LASC) [25], and so on.…”

Section: Introductionmentioning

confidence: 99%

Scene Classification from Synthetic Aperture Radar Images Using Generalized Compact Channel-Boosted High-Order Orderless Pooling Network

Wang

2019

Remote Sensing

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The convolutional neural network (CNN) has achieved great success in the field of scene classification. Nevertheless, strong spatial information in CNN and irregular repetitive patterns in synthetic aperture radar (SAR) images make the feature descriptors less discriminative for scene classification. Aiming at providing more discriminative feature representations for SAR scene classification, a generalized compact channel-boosted high-order orderless pooling network (GCCH) is proposed. The GCCH network includes four parts, namely the standard convolution layer, second-order generalized layer, squeeze and excitation block, and the compact high-order generalized orderless pooling layer. Here, all of the layers are trained by back-propagation, and the parameters enable end-to-end optimization. First of all, the second-order orderless feature representation is acquired by the parameterized locality constrained affine subspace coding (LASC) in the second-order generalized layer, which cascades the first and second-order orderless feature descriptors of the output of the standard convolution layer. Subsequently, the squeeze and excitation block is employed to learn the channel information of parameterized LASC statistic representation by explicitly modelling interdependencies between channels. Lastly, the compact high-order orderless feature descriptors can be learned by the kernelled outer product automatically, which enables low-dimensional but highly discriminative feature descriptors. For validation and comparison, we conducted extensive experiments into the SAR scene classification dataset from TerraSAR-X images. Experimental results illustrate that the GCCH network achieves more competitive performance than the state-of-art network in the SAR image scene classification task.

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“…The use of codebooks and structured representation of images was also considered by Silva et al [14], [15]. However, in this case, local information of grayscale images is extracted by Hessian Affine and SIFT detectors.…”

Section: Related Workmentioning

confidence: 99%

Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval

Lourenço

Silva

Fernandes

2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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In this paper, we present the Hierarchy-of-Visual-Words (HoVW), a novel trademark image retrieval (TIR) method that decomposes images into simpler geometric shapes and defines a descriptor for binary trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical organization of visual data stores each component shape as a visual word. It is capable of representing the geometry of individual elements and the topology of the trademark image, making the descriptor robust against linear as well as to some level of nonlinear transformation. Experiments show that HoVW outperforms previous TIR methods on the MPEG-7 CE-1 and MPEG-7 CE-2 image databases.

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Graph-based bag-of-words for classification

Cited by 52 publications

References 47 publications

Bag of ARSRG Words (BoAW)

Bag of ARSRG Words (BoAW)

Scene Classification from Synthetic Aperture Radar Images Using Generalized Compact Channel-Boosted High-Order Orderless Pooling Network

Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval

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