A novel image retrieval scheme using gray level co-occurrence matrix descriptors of discrete cosine transform based residual image

Varish, Naushad; Pal, Arup Kumar

doi:10.1007/s10489-017-1125-7

Cited by 40 publications

(19 citation statements)

References 43 publications

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“…A good set of features should contain sufficient discrimination power to discriminate image contents. The feature-extraction section uses color coherence vectors (CCV) as the color feature (Roy and Mukherjee, 2013 ), the gray level co-occurrence matrix (GLCM) as the texture feature (Varish and Pal, 2018 ), and introduces the edge histogram descriptor (EHD) feature (Agarwal et al, 2013 ). Among them, CCV is sufficiently robust to handle background complications and invariants in size, orientation, and partial occlusion of the canopy image.…”

Section: Methodsmentioning

confidence: 99%

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

et al. 2018

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The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings.

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Section: Methodsmentioning

confidence: 99%

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

et al. 2018

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“…Gray level co‐occurrence matrix describes the texture features which usually includes energy, entropy, inverse different moment, and correlation and so on by calculating the spatial correlation of image pixels. The energy is the sum of squares of elements’ gray level, which is used to represent the degree of uniformity of gray distribution; the entropy represents the quantity of information of a gray image's texture, that is, the unevenness of image texture; the inverse different moment expresses the homogeneity of image texture, and the correlation reflects the consistency of image texture . The calculation of several parameters of the gray level co‐occurrence matrix could represent the texture features of different cells’ polarization microscopic images and could be effectively applied to distinguish different cells in the spatial domain analysis.…”

Section: Methodsmentioning

confidence: 99%

Detection of non‐small cell lung cancer cells based on microfluidic polarization microscopic image analysis

Wang

Meng

et al. 2018

Electrophoresis

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In early diagnosis of lung cancer, a polarization microscopy is a powerful tool to obtain the optical information of biological tissues. In this paper, a new microfluidic polarization imaging and analysis method was proposed for the detection and classification of cancer‐associated fibroblasts and the two kinds of non‐small cell lung cancer cells, A549 and H322. A polarizing microscopy system was constructed based on a commercial microscope to obtain 3*3 Mueller matrix of cells. Based on the Muller matrix decomposition algorithm and analysis in spatial domain and frequency domain, appropriate classification parameters were selected for the characterization of different polarization characteristics of cells. Finally, the logistic regression models based on machine learning were applied to determine optimal feature parameters and classify cells. This method integrated the morphological information of the cells, and the polarization characteristics of the cells in different polarization states. It is for the first time that the polarization microscopic image analysis method has been applied to the detection and classification of non‐small cell lung cancer cells. The results show that the presented microfluidic polarization microscopic image analysis method could classify cells effectively. Compared with the Muller matrix measurement and calculation methods, the method proposed in this paper was greatly simplified in both the acquisition of polarized images and the analysis and processing of polarized images.

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“…An advantage of the cooccurrence matrix calculations is that the cooccurring pairs of pixels can be spatially related in several orientations related to distance and angular spatial relationships, considering the associations between two pixels at a time. Finally, the mixture of gray levels and the respective positions are determined [26]…”

Section: Gray Level Cooccurrence Matrix Glcmmentioning

confidence: 99%

An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier

Ghrabat

Maolood

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Image retrieval is the process of retrieving images from a database. Certain algorithms have been used for traditional image retrieval. However, such retrieval involves certain limitations, such as manual image annotation, ineffective feature extraction, inability capability to handle complex queries, increased time required, and production of less accurate results. To overcome these issues, an effective image retrieval method is proposed in this study. This work intends to effectively retrieve images using a best feature extraction process. In the preprocessing of this study, a Gaussian filtering technique is used to remove the unwanted data present in the dataset. After preprocessing, feature extraction is applied to extract features, such as texture and color. Here, the texture feature is categorized as a gray level cooccurrence matrix, whereas the novel statistical and color features are considered image intensity-based color features. These features are clustered by k-means clustering for label formation. A modified genetic algorithm is used to optimize the features, and these features are classified using a novel SVMbased convolutional neural network (NSVMBCNN). Then, the performance is evaluated in terms of sensitivity, specificity, precision, recall, retrieval and recognition rate. The proposed feature extraction and modified genetic algorithm-based optimization technique outperforms existing techniques in experiments, with four different datasets used to test the proposed model. The performance of the proposed method is also better than those of the existing (RVM) regression vector machine, DSCOP, as well as the local directional order pattern (LDOP) and color co-occurrence feature + bit pattern feature (CCF + BPF) methods, in terms of the precision, recall, accuracy, sensitivity and specificity of the NSVMBCNN.

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A novel image retrieval scheme using gray level co-occurrence matrix descriptors of discrete cosine transform based residual image

Cited by 40 publications

References 43 publications

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

Detection of non‐small cell lung cancer cells based on microfluidic polarization microscopic image analysis

An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier

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