Roadside vegetation classification using color intensity and moments

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.

One of the important features in content based image retrieval is color feature. The color feature is the most widely used visual features. Extracting feature image depends on the problem to identify the region or object of interest that is complex in content. This paper presents a methodology to recognize certain freshwater images using region of interest and color feature. In this work, we have considered 7 varieties of freshwater fish, Gourami, Mas/Common carper, Mas Orange, Mas Kancra, Mujair/Java Tilapia, Nila/Nile Tilapia, and Patin. Each variety consists of 20 images. We deployed Color Moment Feature after Region of Interest process to extract the feature. Euclid is used for recognition. Considering only a feature, the classification accuracy of 89% is obtained using color moment. The research technique shows promise for eventually being able to do so, and for the future will help to get important information from the image.

Tongue image matching is an important part for a tongue diagnosis system. Unlike common pattern recognition problems, it is hard to define the ground truth for tongue image matching because visual inspection of tongue by doctors is determined by the experience and knowledge of them. Here we propose to use, Mean Rank, as an objective and scientific criterion to evaluate matching performance. Instigating from color demosaicking, a new color texture operator, Primary Difference Signal Local Binary Pattern is proposed. The matching performance is evaluated on color, gray-scale and color texture, and fusion of color and texture features.

We present an efficient approach to index and retrieve Roman mosaic-images by region. The image regions are defined based on the mosaic-image presentation. For more efficiency, the proposed approach uses an index combining texture and fuzzy colour features. These features are fuzzy image dominant colour, fuzzy colour histogram and local Robert gradient binary pattern operator LRGBP. Similar images are extracted using a fuzzy similarity and histogram intersection measures. Our proposed index is tested on database containing 800 images of historical Roman mosaics from the 1st to 4th century. Tests demonstrate that the fusion of the fuzzy colour and texture features increases the precision of the retrieval system. Evaluation tests and comparison with several other prevailing approaches prove a remarkable performance of our proposed approach.

In this study, an identification model based on computer vision and artificial neural network technologies is proposed for the identification of the ripeness of fresh corn ears. For collected images of corn ears, 2D discrete wavelet transform is performed to extract information of low-frequency sub-band as color features, and discrete Fourier transform is performed to extract energy spectrum information as texture features. Principle component analysis is employed for the fusion and dimensionality reduction of color and texture features, and the first three principle components are chosen as inputs of the network model in order to establish probabilistic neural network model for the automated ripeness identification of fresh corn ears. Simulation analysis demonstrates that the identification accuracy of this model reaches 90.67%.

In content based image retrieval (CBIR) process, every image has been represented in a compact set of local visual features i.e. color, texture, and/or shape of images. This set of local visual features is known as feature vector. In the CBIR process, feature vectors of images have been used to represent or to identify similar images in adequate way. As a result, feature vector construction has always been considered as an important issue since it must reflect proper image semantics using minimal amount of data. The proposed CBIR scheme is based on the combination of color and texture features. In this work initially, we have converted the given RGB image into HSV color image. Subsequently, we have considered H (hue), S (saturation), and V (intensity) components for extraction of visual image features. The texture features have been extracted from the V component of the image using 2D dual-tree complex wavelet transform (2D DT-CWT) where it analyzes the textural patterns in six different directions i.e. ±15\(^{\circ }\), ±45\(^{\circ }\), and ±75\(^{\circ }\). At the same time, we have computed the probability histograms of H and S components of the image respectively and subsequently those are divided into non-uniform bins based on cumulative probability for extraction of color based features. So, in this work both the color and texture features have been extracted simultaneously. Finally, the obtained features have been concatenated to attain the final feature vector and same is considered in image retrieval process. We have tested the novelty and performance of the proposed work in two Corel, two objects, and, a texture image datasets. The experimental results reveal the acceptable retrieval performances for different types of datasets.

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high- performance of discrete wavelet transform (DWT). It is time- consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms—Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

Preserved Fruits are one of the famous and traditional Chinese agriculture foods. In this paper, we propose a method that utilizes color and texture features s for Preserved Fruits image classification. We use color moments and subband's statistics of wavelet decomposition as color and texture features respectively. A wide range of Preserved Fruits images are tested to evaluate the performance of the proposed method. The experimental results show that the scheme has produced promising results.

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

Plants are an inevitable part of ecosystem. Tulsi leaves play a major role in plant species. There exists different varieties of tulsi leaves mainly Rama tulsi, Krishna tulsi, Vana tulsi, Kapoor tulsi etc. This paper proposes a system for identifying and classifying different types of tulsi from its leaf images by analyzing color and texture. Rama tulsi, Krishna tulsi and ordinary tulsi are the three varieties of tulsi leaves used for classification. Initially, a dataset is formed from the images of leaves. The feature values of these images are computed and it becomes the training data for the system. When a test image is scanned its color and texture features are extracted. Using K nearest neighborhood algorithm the extracted features are compared with the training data to display category of the leaf. Texture features are obtained using Gray Level Co-occurrence matrix (GLCM) whereas color features are acquired using color moments. Fusions of these techniques are used in this paper.

Striped stem‐borer (SSB) larvae are one of the most serious insect pests that damages rice. In this study, the combined hyperspectral imaging and chemometric method are proposed detect the early SSB larvae infestation in rice stems. Firstly, hyperspectral reflectance data (390–1040 nm) of rice stems were collected for normal, 3 days of infection, 6 days of infection, and 9 days of infection, respectively. On the spectra, the average spectra of the region of interest of the rice stalk samples were extracted, and 19 characteristic wavelengths were screened by the successive projection algorithm (SPA). Morphological processing was performed on the pseudo‐color (generally known as RGB) images of rice stalk samples on the images. Then five image texture features based on the gray‐scale co‐occurrence matrix (GLCM) and nine image color features based on color moments were obtained. The evaluation models of SSB larval infestation based on full spectra, characteristic wavelengths, GLCM, color moments, and data fusion were developed using partial least squares discriminant analysis (PLS‐DA), respectively. The results show that performance of the PLS‐DA model based on the fusion of characteristic wavelengths and GLCM is best. The accuracy of both the comprehensive modeling set and the prediction set is above 97%. The accuracy of the degree of infestation classification is good for all categories, and only the samples on the third day of infection are slightly less accurate. In conclusion, the hyperspectral imaging technology can be used to effectively detect the infection degree situation of early SSB larval infestation.Practical ApplicationsChemical control using insecticides to manage SSB infestation on rice is still the farmers' first choice. However, in the middle and late stages of rice growth, SSB larvae may moth into the inside of the rice stalk, and the rice provides a shelter for SSB so that the sprayed pesticides cannot reach SSB larvae, which produce a large number of overwintering SSB. This leads to the misuse of chemical pesticides, which causes serious pollution to the environment, pesticide residues also cause serious harm to the human body, and it also makes the pest resistance to pesticides rapidly increase. Therefore, it is necessary to find a rapid and effective detection method to detect the early infestation of SSB larvae on rice to reduce the use of pesticides and improve rice yield.

Image classification can be done by using attribute of text that come along with the image, such as file name, size, or creator. Image classification also can be done base on visual content of the image. In this research, we implement a image classification model base on image visual content. The image classification is based on decision tree method that adapt C4.5 algorithm. The decision variable used in the decision tree generation process is image visual features, i.e. color moment order-1, color moment order-2, color moment order-3, entropy, energy, contrast, and homogeneity. The result of this research is an application that can classified image base on the knowledge of the previous classification cases. Keywords: image classification, decision tree, C4.5 algorithm

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%.

A novel method of color and texture feature extraction for image retrieval is proposed. First, an input image is divided into 4×4 nonoverlapping blocks. Then, each block is quantized into two colors by color moment preserving and merging similar colors according to their color difference values under a preset threshold value. A few dominant colors are obtained, and a color histogram of the reconstructed image is computed for the color feature. For the texture feature, by preserving some color moments in the 4×4 neighborhood of each pixel, a bitmap is generated, which can be matched with 64 predefined bitmaps. Then a bitmap spectrum is obtained by counting bitmaps frequencies when all pixels of the input image are considered. An efficient method of similarity measurement is also presented. Experimental results show the proposed method performs well in terms of precision and recall.

Malaria is a severe infectious disease transferred through the bite of an infected mosquito caused by a blood parasite of the genus Plasmodium. In the past, conventional microscopy techniques have proven to be time-consuming and had observed a lack of differentiation due to poor accuracy and a few algorithms used. In this paper, our approach primarily focuses on image processing techniques to process and enhance stained thin blood smear images for feature extraction, as well as machine learning techniques for the final classification of feature space. Our emphasis is also to address the drawbacks, as mentioned earlier, by taking input cell images and performing classification using CNN. As an alternate approach, we have also computed color features using a novel Bins Approach Algorithm, statistical features using color moments, and texture features using GLCM, which also equally played a pivotal role in feature extraction for classification. Further, these images are classified into parasitized and uninfected cells by applying machine learning classifiers such as Linear SVM, Random Forest Algorithm (RNN), and KNN over feature space. The proposed algorithms have been experimented using the subset of Lister Hill National Center for Biomedical Communication (LHNCBC) dataset, a division of the National Library of Medicine (NLM). The performance of the algorithms is evaluated and compared using different parameters like accuracy, precision, recall, and F1-score. The proposed application of Bins Approach in malaria parasite detection has proved better in terms of all parameters as compared to the other existing algorithms.