Tulsi Leaves Classification System

Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal weights. The color moment is used to find nine color features, Gray Level Co-occurrence Matrix (GLCM) to find four texture features, and Hu Moment to find seven shape features totaling 20 neurons and all of these features become the input layer in our MLP network. Three neurons in output layers become the automatic weight of each feature. These weights are used to combine each feature's role in obtaining the relevant image. Euclidean Distance is used to measure similarity. The average precision values obtained using automatic feature weights are 93.94% for the synthetic dataset, 91.19% for the Coil-100 dataset, and 54.31% for the Wang dataset. These results have an average difference of 5.06% with the target so automatic feature weighting works well. This value is obtained at a hidden layer size of 11 and a learning rate of 0.1. In addition, the use of automatic feature weighting gives more accurate results compared to manual feature weighting.

Content-Based Image Retrieval (CBIR) allows to automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. Color, texture and shape information have been the primitive image descriptors in content-based image retrieval systems. This paper presents a fast and efficient image indexing and search system based on color and texture features. The color features are represented by combines 2-D histogram and statistical moments and texture features are represented by combines the direction measure and a gray level co-occurrence matrix (GLCM). The detailed experimental analysis is carried out using precision and recall ondataset Wang. The time analysis is also performed to compare processing speeds of the proposed method with the existing similar best. The experimental results demonstrated that proposed method achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. The performance is measured in terms of recall and precision; also the obtained performances are compared with several state-of-the-art algorithms and showed that our algorithm is simple, fast, and efficient in terms of results and memory. Keywords: Co-occurrence Matrix, Direction Measure, 2-D Histogram, Statistical Moments, CBIR, GLCM

This paper presents an assessment method for the corrosion evaluation of carbon steel. The digital image processing technology is used to extract the texture and color characteristics of rusted carbon steel images. First, Q235 carbon steel images with different degrees of corrosion were collected by digital camera and microscope. Second, the texture features of rusted carbon steel surface are extracted based on Gray Level Co-occurrence Matrix, the color features are extracted using color moments. Third, the extracted features are combined and normalized as the inputs of Support Vector Machine (SVM) for training and classification. We use Particle Swarm Optimization (PSO) algorithm to optimize the SVM classifier. The accuracy of the classification is up to 97.5%.

This research proposed the identification of chicken freshness level based on its color and texture features. Color Features used are the RGB (Red, Green, and Blue) and HSV (Hue, Saturation, Value) channel histogram value. texture features used are GLCM (Grey Level Co-Occurrence Matrix), Gabor kernel, and HOG (Histogram of Oriented Gradients). The freshness level of a chicken meat is categorized into three labels, fresh (0-4 hours after slaughtered), medium-fresh (4-6 hours after slaughtered), and not-fresh (more than 6 hours after slaughtered). The experiments will identify the freshness using several classification methods and different camera resolution and magnification. The highest classification accuracy using SVM (Support Vector Machines) achieves 58,33% with a smartphone camera, 98% with a webcam camera, and 79.1% with a 200 magnification digital microscope. From the experiment results, we can conclude that using webcam camera with normal resolution have better classification accuracy compared with a 200 magnification digital microscope or standard smartphone camera. It is also shown that SVM is superior compared with other methods tested in this experiments which are Decision Tree and Naive Bayes.

Objectives: To make automatic classification of diseased potato and grape leaf from normal potato and grape leaf. Methods: Experimental sample size of 3000 and 4270 Potato and Grape leaf images were used respectively. The diseased and healthy leaf image samples were taken from PlantVillage dataset. The color features viz., average Red, Green, Blue and Hue intensities of Lesion region were calculated. Features namely Contrast, Dissimilarity, Homogeneity, Energy, Correlation, ASM, and Entropy were extracted from hue lesion region. Also, histogram features such as mean and standard deviation were extracted from hue infected region. Then, data normalization was done on feature set to bring all features into a common scale. Finally, Naïve Bayes, K Nearest Neighbor and Support Vector Machine Classifiers were applied on the above said feature sets. Findings: The Dataset was split in the ratio of 80% and 20% for training and test sets. The classifiers NB, KNN and SVM classified Potato leaves with an accuracy of 88.67%, 94.00% and 96.83% respectively and Grape leaves with an accuracy of 81.87%, 93.10% and 96.02% respectively. For both the species, SVM classifier gave the highest accuracy. Also, it was found that the proposed method performs well as compared with the related works in the literature. Novelty/Applications: An effective feature extraction method to classify grape and potato diseases was proposed in this research work. Also, it was found that the proposed method performs well as compared with the related works in the literature. Keywords: RGB color space; HSV color space; histogram; color features; grey-level co-occurrence matrix; texture features

Plant disease is main reason of agricultural crops production losses. Leaf disease in plant occurs due to fungai, virus and bacterias. Image contains various important features which is used in classification. In this paper author initially detect disease then classify disease using extracted features. It takes five diseased leaves (Black rot, Black Measles, Leaf blight, Septoria leaf spot, Bacterial spot) and healthy leaf images then identify leaf is diseased or healthy then if leaf is diseased then classify type of disease. Color features mean, standard deviation, skewness and kurtosis computed then Region based shape feature calculated to identify the size of spots. Texture feature calculated using gray level co-occurrence matrix (GLCM) which identifies texture of image using distance and 45° angle variation in GLCM. Extracted features sent to trained feed forward neural network and classify diseased with color, shape and texture feature individually and combination of all features then observe that combination of color, shape and texture feature improve the performance of classification accuracy.

The Content Based Image Retrieval (CBIR) is a technique that works on images and in response extracts relevant images. A novel hybrid two stage universal CBIR technique using both colour and texture features extraction is proposed in this paper. In the first stage for colour feature extraction, colour moments up to the fourth order are extracted and are used in deriving the respective histograms which forms the colour feature vector. In the second stage for the texture feature extraction the CCM (Colour Co-occurrence Matrix) technique employed takes into account the correlation between the RGB colour bands in all the eight directions while computing the texture features. In every stage the distance between the query image and the image in the database is calculated by using relative distance measure. The resultant distance between the query image and the image in the database is calculated by using a weighted distance classifier. Thus, a hybrid fusion method is achieved that has better performance than other colour-spatial based methods and promises to give more relevant output to the user.KeywordsCBIRlocal statistics histogramsSkewKurtosisCCM

Research in content-based image retrieval today is a lively discipline, expanding in breadth. Discovering new and innovative processes of locating a desired image from an expanding collection of images has been a major area of interest for many professional fields. This has led to the increasing use of a technique called Content Based Image Retrieval which is used to retrieve images based on low-level features which are automatically driven such as color, shape and texture. CBIR draws many of its methods from the field of image processing and computer vision and can be regarded as subset of that field. Using this technique, the entire database may be searched to find the most closely matching image. In this paper, color and texture features of an image have been used to retrieve all the similar images from the image database. For this purpose, Block Color Histogram as color feature and Gray-level Co-occurrence Matrix (GLCM) and Gabor wavelets as texture features have been used. The proposed approach is very efficient since it uses the prominent and distinct features of an image useful for effective image retrieval using fuzzy heuristics according to human visual system.

Radiomics has shown potential in disease diagnosis, but its feasibility for non-small cell lung carcinoma (NSCLC) subtype classification is unclear. This study aims to explore the diagnosis value of texture and colour features from positron emission tomography computed tomography (PET-CT) images in differentiation of NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Two patient cohorts were retrospectively collected into a dataset of 341 18F-labeled 2-deoxy-2fluoro-d-glucose ([18F] FDG) PET-CT images of NSCLC tumours (125 ADC, 174 SqCC, and 42 cases with unknown subtype). Quantification of texture and colour features was performed using freehand regions of interest. The relation between extracted features and commonly used parameters such as age, gender, tumour size, and standard uptake value (SUVmax) was explored. To classify NSCLC subtypes, support vector machine algorithm was applied on these features and the classification performance was evaluated by receiver operating characteristic curve analysis. There was a significant difference between ADC and SqCC subtypes in texture and colour features (P < 0.05); this showed that imaging features were significantly correlated to both SUVmax and tumour diameter (P < 0.05). When evaluating classification performance, features combining texture and colour showed an AUC of 0.89 (95% CI, 0.78–1.00), colour features showed an AUC of 0.85 (95% CI, 0.71–0.99), and texture features showed an AUC of 0.68 (95% CI, 0.48–0.88). DeLong’s test showed that AUC was higher for features combining texture and colour than that for texture features only (P = 0.010), but not significantly different from that for colour features only (P = 0.328). HSV colour features showed a similar performance to RGB colour features (P = 0.473). The colour features are promising in the refinement of NSCLC subtype differentiation, and features combining texture and colour of PET-CT images could result in better classification performance.

Image analysis-based classification of pigmentation patterns in fish: A case study of pseudo-albinism in Senegalese sole

Batik is a traditional craft of designing patterned fabrics which hold high artistic value in Sri Lankan culture, where hand-painted wax patterns are coloured using specialist dyeing methods to create the finished product. This paper presents a study of vision-based feature extraction of Batik images considering colour, texture and shape features to develop a comprehensive feature descriptor of Batik motifs. Wax drawn patterns are identified from the digital images of Batik motifs to retrieve an outline of patterns demarcating the different coloured layers generated by multiple stages of dyeing. Motifs with repetitive patterns are identified using the Local Binary Pattern (LBP) as a texture feature vector. Both RGB and L*a*b* colour schemes are studied in the representation of Batik motifs. The colour description is presented using Mini Batch K-Means which out-performed the widely used K-Means clustering method. Hu Moment Invariants are used for shape feature extraction, and Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. A comprehensive feature descriptor is developed to represent Batik designs, which could be used to recommend similar designs based on the shape and texture features of query images presented by the user.

A new, semantically meaningful technique for querying the images from a spectral image database is proposed. The technique is based on the use of both color- and texture features. The color features are calculated from spectral images by using the Self-Organizing Map (SOM) when methods of Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are used for constructing the texture features. The importance of texture features in a querying is seen in experimental results, which are given by using a real spectral image database. Also the differences between the results gained by the use of co-occurrence matrix and LBP are introduced.

This paper proposes an image retrieval system using the local colour and texture features of object regions and global colour features of the image. The object regions are roughly identified by segmenting the image into fixed partitions and finding the edge density in each partition using edge thresholding and morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region and Euclidean distance measure is used for computing the distance between the features of the query and target image. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods. Also promising results are obtained for 50% and 75% occluded query images.KeywordsContent Based Image RetrievalGLCMColour histogram

In this work, we employ multi-layer feed forward network algorithm for leaf recognition using its shape, color and vein features. The leaf images are pre-processed and segmented. The shape features like area, convex area, diameter, length, width, perimeter, eccentricity, solidity, major axis length and Minor axis length are extracted by taking the features from the segmented leaf image. The mean, standard deviation, skewness and kurtosis for red, green and blue color features are extracted. Wiener filtering and canny edge detection are used to identify the vein features v1, v2, v3, v4 at different thresholds by calculating gray level value from the gray level histogram. The features are then exposed to Principal Component Analysis (PCA) for feature reduction and the resultant reduced feature set contains five shape features, six color features and three vein features. The features are then taken as input vectors for multi-layer neural network. Total 150 leaves from Columbia leaf image database are taken as samples. 150 leaves represent 10 different leaf species. The system was trained with 104 leaf images and was tested and validated using 23 leaf images each. The success rate of reduced feature set of leaf shapes and color was 84.7% and by combining leaf shape, color and vein features outputted a success rate of 95.3%.

According to the recent literature, it is pragmatic that image storage and retrieval through the World Wide Web (WWW) has made impressive progress. Practical searching for image still confronts us with present retrieval systems. A Content Based Image Retrieval (CBIR) system provides an efficient way of retrieving related images from image collections. In this paper we present a new technique to extract the images from the database to achieve better performance and efficiency. The novel method uses an approach, which combines texture information and modified Block Truncation Coding (BTC) method to extract the color features from the image. The whole image is divided into a fixed block of size 4 × 4, 8 × 8, 16 × 16, 32 × 32, 64 × 64, 128 × 128 and 256 × 256 non overlapped blocks. For each block, we calculated the threshold as a mean of the pixel values and the number of pixels which are lower than threshold value and upper than the threshold value are counted and mean of these lower and upper values have been taken and these two are considered as features. Now this process is repeated for the all blocks to extract the features of the image. The second step will compute the four texture features using the Gray-Level Co-occurrence Matrix (GLCM) as a statistical approach to extract energy, contrast, entropy and uniformity. We have used this modified BTC and texture features of the image to retrieve images. Finally, we have conducted experiments on a ground truth image database that has 1000 images of different categories. A different block sizes have been used to demonstrate the experimental results and compared with tabulation. From this we have observed that the performance is better and efficiency is low with smaller block sizes and vice versa with higher block sizes. So we have to choose the middle ranges of block sizes for better performance with better efficiency. As we have observed from the results that the performance of the retrieval system is improved because of combination of texture and color features. The proposed system's performance has been improved by an average precision of 2.41% and recall of 8.22% when compared to existing systems.