Earticle

Computer vision applications in the field of agriculture science are gaining importance. The paper presents a method for recognition of paddy varieties from bulk paddy grain image samples based on color texture features extracted from color co-occurrence matrices. The color texture features are obtained from H, S and I color planes and their combinations. The feature set is reduced based on contribution of features to the recognition accuracy. The reduced feature set of the HS plane includes Energy, Entropy and Correlation features from Hue plane and Energy, Entropy, Contrast, and Correlation features from Saturation plane. The paddy grain images are recognized using a multilayer feed-forward artificial neural network. The considered fifteen paddy varieties have given the recognition accuracy of 92.33%. The work is useful in developing a machine vision system for agriculture produce market and developing multimedia applications in agriculture sciences.

This paper describes Support Vector Machine (SVM) and Artificial Neural Network (ANN) based recognition and classification of visual symptoms affected by fungal disease. Color images of fungal disease symptoms affected on cereals like wheat, maize and jowar are used in this work. Different types of symptoms affected by fungal disease namely leaf blight, leaf spot, powdery mildew, leaf rust, smut are considered for the study. The developed algorithms are used to preprocess, segment, extract features from disease affected regions. The affected regions are segmented using k-means segmentation technique. Color texture features are extracted from affected regions and then used as inputs to SVM and ANN classifiers. The texture analysis is done using Color Co-occurrence Matrix. Tests are performed to classify image samples. Classification accuracies between 68.5% and 87% are obtained using ANN classifier. The average classification accuracies have increased to 77.5% and 91.16% using SVM classifier.

This paper presents a methodology to recognize certain crop fields’ images using texture, color and combination of both types of features. In this work, we have considered eight varieties of crop images, namely, Brinjal, Cotton, Groundnut, Paddy, Soyabean, Sugarcane and Sunflower. Texture features using GLCM and color features using HSV are deployed. Artificial Neural Network (ANN) is used for recognition. Considering only as feature, classification accuracies of 63.75%, 66.25% and 84.375% are obtained using texture, color and their combination respectively. The work is helpful in the area of agriculture for early detection and prevention of diseases.

Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%.

A novel approach of Content Based Image Retrieval(CBIR), which combines color, texture and shape descriptors to represent the features of the image, is discussed in this paper. The proposed scheme is based on three noticeable algorithms: color distribution entropy(CDE), color level co-occurrence(CLCM) and invariant moments. CDE takes the correlation of the color spatial distribution in an image into consideration. CLCM matrix is the texture feature of the image, which is a new proposed descriptor that is grounded on co-occurrence matrix to seize the alteration of the texture. Hu invariant moments are frequently used owing to its invariance under translation, changes in scale, and also rotation. The proposed scheme achieves a modest retrieval result by utilizing these diverse and primitive image descriptors, at the same time, the retrieval result is better when use the texture feature alone which we proposed than use gray level co-occurrence. The similarity measure matrix is based upon Euclidean distance.

In this paper, we present a new texture image retrieval method which combines color and texture features extracted from images by a set of multi-resolution multi-direction (MRMD) filters. The MRMD filter set chosen is simple and can be separable to low and high frequency information, and provides efficient multi-resolution and multi-direction analysis. The color space used is HSV color space separable to hue, saturation, and value components, which are easily analyzed as showing characteristics similar to the human visual system. This experiment is conducted by comparing precision vs. recall of retrieval and feature vector dimensions. Images for experiments include Corel DB and VisTex DB; Corel_MR DB and VisTex_MR DB, which are transformed from the aforementioned two DBs to have multi-resolution images; and Corel_MD DB and VisTex_MD DB, transformed from the two DBs to have multi-direction images. According to the experimental results, the proposed method improves upon the existing methods in aspects of precision and recall of retrieval, and also reduces feature vector dimensions.

기존의 병리 영상을 판독하고 저장, 관리 하는 시스템이 수작업으로 이루어져 발생하는 문제점들을 보완하는 방안으로 유방종양 영상을 사용하여 세포영상 내용기반 검색 시스템을 구축 하고자 한다. 유방암 세포를 사용하여 효율적인 내용기반 영상 검색 시스템을 구축하기 위해서는 유방암 영상에서 검색에 가장 적합한 영상의 질감, 칼라, 형태학적 특징값의 조합이 필요하다. 따라서 본 논문에서는 세포영상의 분류에 많이 사용되는 질감 특징과 칼라값을 사용하여 내용기반 검색 시스템을 구축 하였으며, 칼라값과 질감특징값을 사용하여 검색했을 때의 효율성을 비교하였다. 향후 이런한 실험들을 통하여 세포영상검색에서 가장 최적의 파라미터들을 조합한 내용기반 검색 시스템을 구축하고자 한다.

본 논문에서는 color, texture, shape의 정보를 통합 이용하여 내용기반 영상검색 시스템의 성능을 향상시키는 기법을 고찰하였다. 먼저 영상에 내재되어 있는 color를 분석 추출하여 몇 개의 대표색으로 요약 표현한 다음, 이를 활용한 근사치 측정도를 고안하였다. Texture정보 분석에 있어서는 영상의 주축 행렬 데이터를 통계적 접근 방법으로 추출하였다. Edge분석의 방법으로는 Edge 막대그래프에서 색상변환, 양자화, 필터링에 관련된 정보를 선행처리 후 Edge 정보를 추출하였다. 마지막으로, 본 연구의 결과인 내용기반 영상검색 시스템의 효율성을 precision-recall 분석과 실험적 결과를 통하여 입증하였다.

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.