Approach of Using Texture and Shape for Image Retrieval

2013 ◽  
pp. 30-45
Author(s):  
Gang Zhang ◽  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Feature Integration ◽  
Shape Features ◽  
Important Research ◽  
Information Redundancy ◽  
Single Feature ◽  
Descriptor Approach

Feature integration is one of important research contents in content-based image retrieval. Single feature extraction and description is foundation of the feature integration. Features from a single feature extraction approach are a single feature or composite features, whether integration features are more discriminative than them or not. An approach of integrating shape and texture features was presented and used to study these problems. Gabor wavelet transform with minimum information redundancy was used to extract texture features, which would be used for feature analyses. Fourier descriptor approach with brightness was used to extract shape features. Then both features were integrated in parallel by weights. Comparisons were carried out among the integration features, the texture features, and the shape features, so that discrimination of the integration features can be testified.

Image Recognition Based on Shape and Texture Features

2014 ◽  
Vol 536-537 ◽  
pp. 127-130
Author(s):  
Kun Geng
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Retrieval System ◽  
Texture Features ◽  
Morphological Processing ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Key Technologies ◽  
Image Retrieval System ◽  
Important Position

Based on the shape of the image retrieval occupy an important position in the content-based image retrieval, and studied architecture, content-based image retrieval system, ie research-based image retrieval key technologies shape features for image noise in addition to the morphological processing; image segmentation; shape-based feature extraction and regional boundaries and description techniques and similarity measure techniques. The results show that the algorithm can effectively identify the characteristics of the image.

Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval

2011 ◽  
pp. 44-67 ◽  
Author(s):  
Gang Zhang ◽  
Z. M. Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Image ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Texture Feature Extraction ◽  
Medical Image Retrieval ◽  
Image Retrieval System ◽  
Occurrence Matrix

Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first proposes a framework of content-based medical image retrieval system. It then analyzes the important texture feature extraction and description methods further, such as the co-occurrence matrix, perceptual texture features, Gabor wavelet, and so forth. Moreover, the chapter analyzes the improved methods for these methods and demonstrates their application in content-based medical image retrieval.

Content-Based Image Retrieval through Region Uniformly Partition

2012 ◽  
Vol 500 ◽  
pp. 471-474 ◽  
Author(s):  
Xiao Xiao ◽  
De Wen Zhuang ◽  
Shou Jue Wang
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Similarity Measure ◽  
Open Problem ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Content Based Image Retrieval ◽  
Retrieval Performance ◽  
Color Features ◽  
Better Than

It has been demonstrated that accurate image segmentation is still an open problem. For avoiding this difficulties in content-based image retrieval, an region uniform partition approaching was proposed. Based on fusing regional color features using smooth slide histogram and texture features extracted using Gabor wavelet, we provided the corresponding similarity measure. The image retrieval performance on a subset of the COREL database are better than SIMPLIcity system showed the effectiveness of the proposed method.

scholarly journals A Facial Expression Recognition Model Based on Texture and Shape Features

2020 ◽  
Vol 37 (4) ◽  
pp. 627-632
Author(s):  
Aihua Li ◽  
Lei An ◽  
Zihui Che
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Shape Features ◽  
The Face ◽  
Facial Expression Classification

With the development of computer vision, facial expression recognition has become a research hotspot. To further improve the accuracy of facial expression recognition, this paper probes deep into image segmentation, feature extraction, and facial expression classification. Firstly, the convolution neural network (CNN) was adopted to accurately separate the salient regions from the face image. Next, the Gaussian Markov random field (GMRF) model was improved to enhance the ability of texture features to represent image information, and a novel feature extraction algorithm called specific angle abundance entropy (SAAE) was designed to improve the representation ability of shape features. After that, the texture features were combined with shape features, and trained and classified by the support vector machine (SVM) classifier. Finally, the proposed method was compared with common methods of facial expression recognition on a standard facial expression database. The results show that our method can greatly improve the accuracy of facial expression recognition.

scholarly journals AN OPTIMIZED FEATURE EXTRACTION TECHNIQUE FOR CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 11-16
Author(s):  
SAVITHA SIVAN ◽  
THUSNAVIS BELLA MARY. I
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Fusion ◽  
Texture Features ◽  
Research Area ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Multimedia Technologies ◽  
Active Research ◽  
Occurrence Matrix

Content-based image retrieval (CBIR) is an active research area with the development of multimedia technologies and has become a source of exact and fast retrieval. The aim of CBIR is to search and retrieve images from a large database and find out the best match for the given query. Accuracy and efficiency for high dimensional datasets with enormous number of samples is a challenging arena. In this paper, Content Based Image Retrieval using various features such as color, shape, texture is made and a comparison is made among them. The performance of the retrieval system is evaluated depending upon the features extracted from an image. The performance was evaluated using precision and recall rates. Haralick texture features were analyzed at 0 o, 45 o, 90 o, 180 o using gray level co-occurrence matrix. Color feature extraction was done using color moments. Structured features and multiple feature fusion are two main technologies to ensure the retrieval accuracy in the system. GIST is considered as one of the main structured features. It was experimentally observed that combination of these techniques yielded superior performance than individual features. The results for the most efficient combination of techniques have also been presented and optimized for each class of query.

Multi-Featured Content-Based Image Retrieval Using Color and Texture Features

2016 ◽  
Vol 850 ◽  
pp. 136-143 ◽  
Author(s):  
Mehmet Ayan ◽  
O. Ayhan Erdem ◽  
Hasan Şakir Bilge
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Color Histogram ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Occurrence Matrix ◽  
Color Moment ◽  
Color And Texture Features

Content-based image retrieval (CBIR) system becomes a hot topic in recent years. CBIR system is the retrieval of images based on visual features. CBIR system based on a single feature has a low performance. Therefore, in this paper a new content based image retrieval method using color and texture features is proposed to improve performance. In this method color histogram and color moment are used for color feature extraction and grey level co-occurrence matrix (GLCM) is used for texture feature extraction. Then all extracted features are integrated for image retrieval. Finally, color histogram, color moment, GLCM and proposed methods are tested respectively. As a result, it is observed that proposed method which integrates color and texture features gave better results than the other methods used independently. To demonstrate the proposed system is successful, it was compared with existing CBIR systems. The proposed method showed superior performance than other comparative systems.

scholarly journals Apakah kombinasi power lbp dan fourier descriptor dapat digunakan untuk klasifikasi citra kerang?

Teknologi ◽  
2016 ◽  
Vol 6 (2) ◽  
pp. 68
Author(s):  
Putri Aisyiyah Rakhma Devi ◽  
Nanik Suciati ◽  
Wijayanti Nurul Khotimah
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Cross Validation ◽  
Texture Features ◽  
Extraction Process ◽  
Binary Image ◽  
Grayscale Image ◽  
Shape Features ◽  
Fourier Descriptor ◽  
Classification Problems

ABSTRAKPermasalahan pengklasifikasian secara manual biasanya terletak pada hasil akurasi dan waktu klasifikasi. Pengklasifikasi citra kerang pada umumnya dilakukan berdasarkan pada karakteristik bentuk dan tekstur cangkang kerang. Pengembangan perangkat lunak untuk pengklasifikasian secara otomatis diharapkan dapat meningkatkan hasil akurasi dan memperbaiki waktu klasifikasi. Pada penelitian ini bertujuan untuk mengkombinasikan fitur tekstur berbasis metode Power LBP dan fitur bentuk berbasis metode fourier descriptor yang digunakan untuk klasifikasi citra kerang.Citra input yang digunakan, sebelumnya telah melalui praproses dan  segmentasi untuk memisahkan objek dengan background. Citra objek yang sudah terpisah ditransformasi menjadi citra biner dan citra grayscale untuk proses ekstraksi fitur. Hasil dari kedua fitur yang sudah diperoleh akan dilakukan kombinasi dengan mempertimbangkan bobot masing-masing fitur yang kemudian dilakukan normalisasi. Dengan mengkombinasikan fitur tekstur dan fitur bentuk diharapkan memperoleh fitur yang signifikan yang dapat meningkatkan akurasi sebuah klasifikasi.Uji coba dilakukan pada 3 jenis dataset kerang yakni kerang darah, kerang pasir dan kerang bulu dengan menggunakan SVM cross validation dengan k=2 . Hasil uji coba menunjukkan bahwa ada keterkaitan antara mengkombinasikan fitur tekstur dan fitur bentuk pada permasalahan klasifikasi citra kerang dapat diperbaiki dengan hasil akurasi klasifikasi yang diperoleh sebesar 99,39% dengan fitur tekstur lebih dominan daripada fitur yang lainnya. Kata Kunci: citra kerang, ekstraksi fitur, fourier descriptor, klasifikasi, power LBP. ABSTRACTShells image classification are generally conducted based on the characteristics of the shape and texture of the shells. The problems of classification usually occur results of accuracy and timing classification. The software development for classification is expected to increase the yield of accuracy result and optimize the time of classification. In this study, we combine extracting texture features based Power LBP method and extracting shape features based Fourier Descriptor method for shells image classification.   The used input images had been conducted preprocessing  and segmentation to separate object and background using Otsu methods. The objects images that had been separated are transformed into a binary image and grayscale image for feature extraction process. Texture features are extracted using Power LBP (PLBP) method and grayscale image as input. Shape features are extracted using Fourier Descriptor (FD) method and binary image as input. The results of these two features will be combined by considering the weight of each feature and then normalized. Combines texture features and shape features, we expect to obtain significant features that can improve the accuracy of classification.Tests was performed on three types of shells dataset that is blood clams, mussels and scallops feather sand by using SVM cross validation with k = 2 fold. The results show that there is a link between features combine texture and shape features on the image classification problems that can be solved with the results obtained classification accuracy of 99.39% with a texture feature more dominant than the other features. Keywords: classification, feature extraction, Fourier Descriptor , Power LBP, Shellfish image.

scholarly journals Content Based Image Retrieval using Texture, Color and Shape for Image Analysis

2012 ◽  
Vol 3 (1) ◽  
pp. 149-152 ◽  
Author(s):  
Amanbir Sandhu ◽  
Aarti Kochhar
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Extraction Techniques ◽  
Low Level ◽  
The Past ◽  
Single Feature ◽  
Color Texture

Content- Based Image Retrieval(CBIR) or QBIR  is the important  field of research..Content  Based Image retrieval has gained much popularity  in the past Content-based image retrieval (CBIR)[1] system has also helped users to retrieve relevant images based on their contents. It represents low level features like texture ,color and shape .In this paper, we compare the several feature extraction techniques [5]i.e..GLCM ,Histogram and shape properties  over color,  texture and shape The experiments show the similarity between these features and also that the output obtained using this combination of color, texture and shape is better as obtaining output  with a single feature

scholarly journals A content-based image retrieval system for hat database

2021 ◽  
Author(s):  
Danhua Li
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Gaussian Mixture ◽  
Middle Level ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Fourier Descriptor ◽  
Low Level ◽  
Two Component ◽  
High Level

Content-based image retrieval (CBIR) is a technique for indexing and retrieving images based on the low-level features, middle-level features, and high-level features. Low-level feature is extracted from contents of the images such as color, texture and shape; middle-level feature is a region obtained as a result of image segmentation; high-level feature is semantic information about the meaning of image, its objects and their roles, and categories to which the image belongs. In this project, three low-level features texture-based retrieval, color-based retrieval and shape-based retrieval are implemented and compared on hat database. Texture features are obtained from parameters of a two-component Gaussian mixture model (GMM) in the wavelet domain. Color features are extracted from a two-component GMM on HLS color space. Shape features are extracted from the contour by using centroid-contour distance Fourier descriptor. A comprehensive experimental evaluation of the retrieval performance of different feature sets is performed. The experimental results indicate that the shape features based on the centroid-contour distance Fourier descriptor perform much better than the color and texture features for the hat database used in this project

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