Identification of Canola Seeds using Nearest Neighbor and K-Nearest Neighbor Algorithms

Agriculture plays an important role on Pakistan economy. Canola is the major crop of Pakistan. There are different varieties of canola crop. It fulfills the requirement of oil. It is the difficult task to identify best canola seeds for sowing due to different varieties of canola seeds. In this paper we are try to introduce different machine learning approaches for classification of different canola seeds which provide opportunity to people or farmer to identify different canola seeds. Canola seeds verities implementing by the computer vision image processing tool. We have the 4 different varieties which names as Gobhi Sarson (A), Barassica comp (B), Sathri (C) and Rocket Herbof (D) canola seeds and take the images of canola seeds from these different varieties. Each variety has 10 images and we have total 10*4 =40 images of canola seeds. we take the train and test data results of all kinds of canola seeds. then train and test data results will be compare for pattern classification and apply the nearest neighbor and k-nearest neighbor algorithms for final classification in computer image processing tool. We achieved in nearest neighbor 85% and 76% average and k-nearest neighbor 90% and 73% average as a final pattern classification results. These are the best percentage for classification and provide more accuracy. These are important for farmer and other people for identify the different canola seeds. Keywords:  Features, Pattern classification, nearest neighbor, k-nearest neighbo

Bark Classification of Trees Using K-Nearest Neighbor & Nearest Neighbor Algorithms

Pakistan is an agricultural country and less than 4 % of area secured with forests. Tree automatic classification based on computer science and it is the developing trend of classification. In this paper we examine how we can done bark classification of trees using k-nearest neighbor and nearest neighbor algorithms. There we discuss how these algorithms can be used to automatically classify trees from images of bark. We get the images of five kinds of different trees names suppose as A, B, C, D and E through using digital camera. We take ten different images of each kind of trees. The capability and information of inspectors are essential to perfectly achieve this process. The all the process will be done in computer vision image processing tool. In this tool we use the Histogram Features, Texture Features, and Pattern Classification. We achieved the final results of five kinds of different trees using nearest neighbor on distance two 82% average and on k-nearest neighbor when k=2 then the average result 82%, when k=3 the average result 82%, when k=4 then the average result 76% and when k=5 the average percentage 72% the result shows the maximum correct result and classifies the trees. These are the best percentage results using these algorithms for classification. In this way we can easily classify the different trees and also these methods provide opportunity to farmer and other people for identify and select the different better different trees for getting more benefit. Keywords: CVIP Tool, Histogram Features, Texture Features, Pattern Classification, Classification Algorithm

Dimensionality reduction by minimizing nearest-neighbor classification error

There is a great interest in dimensionality reduction techniques for tackling the problem of high-dimensional pattern classification. This paper addresses the topic of supervised learning of a linear dimension reduction mapping suitable for classification problems. The proposed optimization procedure is based on minimizing an estimation of the nearest neighbor classifier error probability, and it learns a linear projection and a small set of prototypes that support the class boundaries. The learned classifier has the property of being very computationally efficient, making the classification much faster than state-of-the-art classifiers, such as SVMs, while having competitive recognition accuracy. The approach has been assessed through a series of experiments, showing a uniformly good behavior, and competitive compared with some recently proposed supervised dimensionality reduction techniques. © 2010 Elsevier B.V. All rights reserved.Work partially supported by the Spanish projects TIN2008-04571 and Consolider Ingenio 2010: MIPRCV (CSD2007-00018).Villegas, M.; Paredes Palacios, R. (2011). Dimensionality reduction by minimizing nearest-neighbor classification error. Pattern Recognition Letters. 32(4):633-639. https://doi.org/10.1016/j.patrec.2010.12.002S63363932

Adaptive imputation of missing values for incomplete pattern classification

In classification of incomplete pattern, the missing values can either play a crucial role in the class determination, or have only little influence (or eventually none) on the classification results according to the context. We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory. At first, we try to classify the object (incomplete pattern) based only on the available attribute values. As underlying principle, we assume that the missing information is not crucial for the classification if a specific class for the object can be found using only the available information. In this case, the object is committed to this particular class. However, if the object cannot be classified without ambiguity, it means that the missing values play a main role for achieving an accurate classification. In this case, the missing values will be imputed based on the K-nearest neighbor (K-NN) and self-organizing map (SOM) techniques, and the edited pattern with the imputation is then classified. The (original or edited) pattern is respectively classified according to each training class, and the classification results represented by basic belief assignments are fused with proper combination rules for making the credal classification. The object is allowed to belong with different masses of belief to the specific classes and meta-classes (which are particular disjunctions of several single classes). The credal classification captures well the uncertainty and imprecision of classification, and reduces effectively the rate of misclassifications thanks to the introduction of meta-classes. The effectiveness of the proposed method with respect to other classical methods is demonstrated based on several experiments using artificial and real data sets

Thermography based breast cancer detection using texture features and minimum variance quantization

In this paper, we present a system based on feature extraction techniques and image segmentation techniques for detecting and diagnosing abnormal patterns in breast thermograms. The proposed system consists of three major steps: feature extraction, classification into normal and abnormal pattern and segmentation of abnormal pattern. Computed features based on Gray Level Co-occurrence Matrices (GLCM) are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 GLCM features are extracted from thermograms. The ability of feature set in differentiating abnormal from normal tissue is investigated using a Support Vector Machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross validation method and Receiver operating characteristic analysis was performed. The verification results show that the proposed algorithm gives the best classification results using K-Nearest Neighbor classifier and a accuracy of 92.5 %. Image segmentation techniques can play an important role to segment and extract suspected hot regions of interests in the breast infrared images. Three image segmentation techniques: minimum variance quantization, dilation of image and erosion of image are discussed. The hottest regions of thermal breast images are extracted and compared to the original images. According to the results, the proposed method has potential to extract almost exact shape of tumors

Classification of Gender Individual Identification Using Local Binary Pattern on Palatine Rugae Image

Major disasters caused many casualties with the condition of the damaged bodies. It causes the individual identification process to be ineffective through biometric characteristics (such as lips and fingerprints). However, the palatine rugae can carry the individual identification process. Palatine rugae have unique and individual characteristics and are more resistant to trauma because of their internal location. In this study, an individual identification system is proposed to identify gender using the image of palatine rugae. The proposed system is developed by several algorithms and methods, such as Local Binary Pattern (LBP) as the feature extraction method and K-Nearest Neighbor (KNN) as the classification method. Based on the result of the system performed test, the proposed system can identify the gender of an individual by the combination of recognized palatine rugae patterns. The system achieved an accuracy test result of 100% with a specific configuration of LBP and KNN. The research contribution in this study is to develop the individual gender identification system, which proceeds with the palatine rugae pattern image with unique biometric characteristics as an input. The system applied several methods and algorithms, such as Geometric Active Contour (GAC) as a segmentation algorithm, Local Binary Pattern (LBP) as a feature extraction method, and K Nearest Neighbor (KNN) as a classification method

Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions

The paper presents the results of the automatic classification of rock images, taken under an optical microscope under different lighting conditions and with different polarization angles. The classification was conducted with the use of four pattern recognition methods: nearest neighbor, k-nearest neighbors, nearest mode, and optimal spherical neighborhoods on thin sections of five selected rocks. During research the CIELAB color space and the 9D feature space were used. The results indicate that changing both lighting conditions and polarization angles results in worsening the classification outcome, although not substantially. Duduring the automatic classification of rocks photographed under different lighting and polarization conditions, the highest number of correctly classified rocks (97%) is given by the nearest neighbor method. The results show that the automatic classification of rocks is possible within a pre-defined group of rocks. The results also indicate the optimal spherical neighborhoods method to be the safest method out of those tested, which means that it returns the lowest number of incorrect classifications