A new subspace based solution to background modelling and change detection

Işık, Şahin; Özkan, Kemal; Gerek, Ömer Nezih

doi:10.18201/ijisae.267148

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Similarly, an attempt had been realized in works of CVA [28] with a purpose to fuse multispectral channels. Until now, the CVA has been performed for different tasks ranging from classification [29] to image processing [30,31]. As a subspace based projection technique, the CVA method can be summarized as follows [32].…”

Section: Fusion Of Swir Vnir and Rgbmentioning

confidence: 99%

Consensus rule for wheat cultivar classification on VL, VNIR and SWIR imaging

Işık

Özkan

Demirez

et al. 2021

IET Image Processing

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To facilitate the quality assessment of wheat cultivars, diverse imaging tools and techniques have been applied in order to omit the expert decision, which can cause failure in the identification of the wheat cultivar's label and its quality simultaneously. To minimize the risks caused by the expert's decision, a promising framework for identification is greatly required to more effectively assess wheat type. Therefore, to be beneficial to this association, two methods have been developed by performing traditional and modern feature extraction algorithms on visible light (VL), visible near-infrared (VNIR) and short-wave infrared (SWIR) imaging as well as a fusion of these imaging systems. The proposed systems are called the bag of word (BoW) framework and convolutional neural networks (CNN) framework. With regard to wheat cultivar detection, the consensus rule has been established based on decisions predicted by CNN and BoW frameworks. The accuracy results obtained by consensus rule indicate that we have achieved 99.94% and 68.94% in case of CNN framework and BoW framework, respectively. Experimental results suggest that BoW features are not suitable to represent and match texture patterns such as repeated wheat kernels in an image, whereas CNN features always outperform handcrafted elements and properties for all datasets.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Fusion Of Swir Vnir and Rgbmentioning

confidence: 99%

Consensus rule for wheat cultivar classification on VL, VNIR and SWIR imaging

Işık

Özkan

Demirez

et al. 2021

IET Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The CVA is a technique applied in speech and image recognition tasks to model class-invariant properties. It is used for background subtraction [33], face recognition [34] and isolated speech recognition tasks [35]. As discussed in the introduction section, the CVA is an efficient strategy for differentiating unique author properties from the domain.…”

Section: Bucketed CV Scalingmentioning

confidence: 99%

Bucketed common vector scaling for authorship attribution in heterogeneous web collections: A scaling approach for authorship attribution

Agun

Yılmazel

2019

Journal of Information Science

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Domain, genre and topic influences on author style adversely affect the performance of authorship attribution (AA) in multi-genre and multi-domain data sets. Although recent approaches to AA tasks focus on suggesting new feature sets and sampling techniques to improve the robustness of a classification system, they do not incorporate domain-specific properties to reduce the negative impact of irrelevant features on AA. This study presents a novel scaling approach, namely, bucketed common vector scaling, to efficiently reduce negative domain influence without reducing the dimensionality of existing features; therefore, this approach is easily transferable and applicable in a classification system. Classification performances on English-language competition data sets consisting of emails and articles and Turkish-language web documents consisting of blogs, articles and tweets indicate that our approach is very competitive to top-performing approaches in English competition data sets and is significantly improving the top classification performance in mixed-domain experiments on blogs, articles and tweets.

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A new subspace based solution to background modelling and change detection

Cited by 2 publications

References 22 publications

Consensus rule for wheat cultivar classification on VL, VNIR and SWIR imaging

Consensus rule for wheat cultivar classification on VL, VNIR and SWIR imaging

Bucketed common vector scaling for authorship attribution in heterogeneous web collections: A scaling approach for authorship attribution

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