A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features

Ma, Jun; Ma, Ziping; Kang, Baosheng; Lü, Ke

doi:10.1155/2014/269394

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…The SURF (speeded-up robust features) descriptor algorithm [51] allows extracting features (~1000) from these images. Thousands of variables are then extracted in each stand to construct a 500-dimensional BoVW vector, containing the most influential features (~500) owing the Bag of Visual World method [27,52,53]. This approach is very popular in agriculture to discriminate between crop and weeds [8,27,51].…”

Section: Image Processingmentioning

confidence: 99%

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

Gée

Denimal

2020

Remote Sensing

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In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

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Section: Image Processingmentioning

confidence: 99%

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

Gée

Denimal

2020

Remote Sensing

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“…For example, the DALI Z-score [1,8], the Local Feature Profile [9], and the Effective Moment Feature Vector [10], which were generated as three-dimensional maps by dimensionality reduction, have shown that all α-, all β-, α/ β-, and α+β-domains are grouped. Moreover, multi-view rendering of 3D protein structures was used to obtain a 2D image followed by feature extraction [11]. In this method, the codebook is generated by clustering visual words and a histogram is used to explore similarities between protein structures.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of visual codewords of a protein distance matrix

Pražnikar

Attygalle

2022

PLoS ONE

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3D protein structures can be analyzed using a distance matrix calculated as the pairwise distance between all Cα atoms in the protein model. Although researchers have efficiently used distance matrices to classify proteins and find homologous proteins, much less work has been done on quantitative analysis of distance matrix features. Therefore, the distance matrix was analyzed as gray scale image using KAZE feature extractor algorithm with Bag of Visual Words model. In this study, each protein was represented as a histogram of visual codewords. The analysis showed that a very small number of codewords (~1%) have a high relative frequency (> 0.25) and that the majority of codewords have a relative frequency around 0.05. We have also shown that there is a relationship between the frequency of codewords and the position of the features in a distance matrix. The codewords that are more frequent are located closer to the main diagonal. Less frequent codewords, on the other hand, are located in the corners of the distance matrix, far from the main diagonal. Moreover, the analysis showed a correlation between the number of unique codewords and the 3D repeats in the protein structure. The solenoid and tandem repeats proteins have a significantly lower number of unique codewords than the globular proteins. Finally, the codeword histograms and Support Vector Machine (SVM) classifier were used to classify solenoid and globular proteins. The result showed that the SVM classifier fed with codeword histograms correctly classified 352 out of 354 proteins.

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Image classification using SURF and bag of LBP features constructed by clustering with fixed centers

Srivastava

Rajitha

Agarwal

2018

Multimed Tools Appl

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A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features

Cited by 5 publications

References 35 publications

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

Quantitative analysis of visual codewords of a protein distance matrix

Image classification using SURF and bag of LBP features constructed by clustering with fixed centers

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