Image classification by combining local and global features

Kabbai, Leila; Abdellaoui, Mehrez; Douik, Ali

doi:10.1007/s00371-018-1503-0

Cited by 72 publications

(28 citation statements)

References 41 publications

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“…Table 5 shows the comparison between our approach and state-of-the-art methods. Our approach could increase the accuracy compared to feature extraction models, such as LLKc [33], N 3 SC encoder [29], and CLGC(RGB-RGB) [31]. Although the accuracy of our approach was lower than deep learning models, such as Hybrid-CNN [25], TPN-FS [34], and DeepSCNet [35], it could reduce computational cost compared to deep learning methods, which suggest that it is still useful in real-time processing systems.…”

Section: Uiuc-sports Datasetmentioning

confidence: 93%

“…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%

“…Local Soft Assignment [39] 82.3% LLKc [33] 86.4% N 3 SC encoder [29] 85.5% CLGC(RGB-RGB) [31] 86.4% MUSIC [40] 81.8% OB2014 [41] 82.3% Hybrid-CNN [25] 94.2% TPN-FS [34] 95.2% DeepSCNet [35] 87.1% FScSPM (Our Approach) 86.7%…”

Section: Avg Accuracymentioning

confidence: 99%

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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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Section: Uiuc-sports Datasetmentioning

confidence: 93%

“…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%

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A Fast Sparse Coding Method for Image Classification

et al. 2019

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“…The category-wise classification of digital images is considered as one of the main requirement in computer vision applications such as scene analysis, remote sensing, medical science and image retrieval [ 1 – 7 ]. The changes in scale, illumination, rotations, overlapping objects, appearance of same view in the images of different classes, complex structures and difference in image spatial atterns make image classification an open research problem [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The changes in scale, illumination, rotations, overlapping objects, appearance of same view in the images of different classes, complex structures and difference in image spatial atterns make image classification an open research problem [ 8 ]. In past, global spatial features such as color and texture were used to perform image classification [ 1 ]. The low computational cost and simple implementation were considered as the main advantages of global spatial features [ 1 ].…”

Section: Introductionmentioning

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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