scholarly journals A Review on LBP in Image Retrieval System for Future Enhancement and Vector Images

2020 ◽  
pp. 155-161
Author(s):  
K Rajalakshmi ◽  
V Krishna Dharshini ◽  
S Selva Meena
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Query Image ◽  
Matching Algorithm ◽  
Image Retrieval System ◽  
Similar Images ◽  
Vector Images ◽  
Similar Kind

Content-Based Image Retrieval is a process to retrieve the similar images from the large set of image database corresponding to the query image. In CBIR low level or pixel level features such as color, texture and shape of the images are extracted and on the basis of similarity matching algorithm the required similar kind of images are retrieved from the image database. To understand the evaluation and evolution of CBIR system various research was studied and various research is going on this way also. In this paper, we have discussed some of the popular pixel level feature extraction techniques for Content-Based Image Retrieval and we also present here about the performance of each technique.

scholarly journals Content-Based Image Retrieval System for Real Images

10.29007/w4sr ◽  
2018 ◽  
Author(s):  
Yin-Fu Huang ◽  
Bo-Rong Chen
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
Multimedia Data ◽  
Content Based Image Retrieval ◽  
Real Image ◽  
Query Image ◽  
Image Retrieval System ◽  
Similar Images ◽  
Network Technologies

With the rapid progress of network technologies and multimedia data, information retrieval techniques gradually become content-based, and not text-based yet. In this paper, we propose a content-based image retrieval system to query similar images in a real image database. First, we employ segmentation and main object detection to separate the main object from an image. Then, we extract MPEG-7 features from the object and select relevant features using the SAHS algorithm. Next, two approaches “one-against- all” and “one-against-one” are proposed to build the classifiers based on SVM. To further reduce indexing complexity, K-means clustering is used to generate MPEG-7 signatures. Thus, we combine the classes predicted by the classifiers and the results based on the MPEG-7 signatures, and find out the similar images to a query image. Finally, the experimental results show that our method is feasible in image searching from the real image database and more effective than the other methods.

scholarly journals Unsupervised Content Based Image Retrieval by Combining Visual Features of an Image With A Threshold

2012 ◽  
pp. 149-154
Author(s):  
S. M. Zakariya ◽  
Rashid Ali ◽  
Nesar Ahmad
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Query Image ◽  
System A ◽  
Image Retrieval System

Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape and texture to represent and index the image. In a typical content based image retrieval system, a set of images that exhibit visual features similar to that of the query image are returned in response to a query. CLUE (CLUster based image rEtrieval) is a popular CBIR technique that retrieves images by clustering. In this paper, we propose a CBIR system that also retrieves images by clustering just like CLUE. But, the proposed system combines all the features (shape, color, and texture) with a threshold for the purpose. The combination of all the features provides a robust feature set for image retrieval. We evaluated the performance of the proposed system using images of varying size and resolution from image database and compared its performance with that of the other two existing CBIR systems namely UFM and CLUE. We have used four different resolutions of image. Experimentally, we find that the proposed system outperforms the other two existing systems in ecery resolution of image.

scholarly journals Proposed a Content-Based Image Retrieval System Based on the Shape and Texture Features

2019 ◽  
Vol 8 (11) ◽  
pp. 2185-2192
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Visual Image ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Feature Extraction Method ◽  
Image Retrieval System ◽  
Reference Images ◽  
Similar Images

In this paper, we proposed a fusion feature extraction method for content based image retrieval. The feature is extracted by focusing on the texture and shape features of the visual image by using the Local Binary Pattern (LBP – texture feature) and Edge Histogram Descriptor (EHD – shape feature). The SVD is used for decreasing the number of the feature vector of images. The Kd-tree is used for reducing the retrieval time. The input to this system is a query image and Database (the reference images) and the output is the top n most similar images for the query image. The proposed system is evaluated by using (precision and recall) to measure the retrieval effectiveness. The values of the recall are between [43% –93%] and the average recall is 64.3%. The values of precision are between [30%-100%] and the average is 72.86% for the entire system and for both databases

scholarly journals Content Based Image Retrieval (CBIR) by Statistical Methods

2020 ◽  
Vol 17 (2(SI)) ◽  
pp. 0694
Author(s):  
Fathala Ali et al.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Semantic Content ◽  
T Test ◽  
Statistical Characteristics ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Image Retrieval System ◽  
Level Of Significance ◽  
Second Procedure

            An image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T- test to measure the independence between more than images, (coefficient of correlate, T- test, Level of significance, find the decision), and, through experimental test, it was found that this proposed method of retrieval technique is powerful than the classical retrieval System.

scholarly journals An Efficient Two-layer based Technique for Content-based Image Retrieval

2021 ◽  
Vol 5 (1) ◽  
pp. 28
Author(s):  
Fawzi Abdul Azeez Salih ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Color Spaces ◽  
Query Image ◽  
Current State ◽  
Similar Images ◽  
Different Color

The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.

Content-based Image Retrieval by Information Theoretic Measure

2011 ◽  
Vol 61 (5) ◽  
pp. 415 ◽  
Author(s):  
Madasu Hanmandlu ◽  
Anirban Das
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Texture Features ◽  
Entropy Function ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Information Theoretic ◽  
Dominant Colour ◽  
Image Retrieval System ◽  
Information Theoretic Measures

<p>Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.415-430</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1177</strong></strong></p>

scholarly journals Knn And Steerable Pyramid Based Enhanced Content Based Image Retrieval Mechanism

2018 ◽  
Vol 17 (2) ◽  
pp. 7215-7225
Author(s):  
Bohar Singh ◽  
Mrs. Mehak Aggarwal
Keyword(s):  
Image Retrieval ◽  
Image Data ◽  
Texture Features ◽  
Content Management ◽  
Content Based Image Retrieval ◽  
Visual Content ◽  
Image Region ◽  
Query Image ◽  
Image Retrieval System ◽  
Steerable Pyramid

Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. There has been an increase in attention towards the automated management and retrieval of digital images owing to the drastic development in the number and size of image databases. A significant and increasingly popular approach that aids in the retrieval of image data from a huge collection is called Content-based image retrieval (CBIR). Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this thesis work, we present a steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image region. To speed up retrieval and similarity computation, the database images are classified and the extracted regions are clustered according to their feature vectors using KNN algorithm We have used steerable pyramid to extract texture features from query image and classified database images and store them in feature features. Therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity.  Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time.

scholarly journals Content based Image Retrieval using Statistical Parameters of a Medical Image

2019 ◽  
Vol 8 (3) ◽  
pp. 3649-3653
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Content Based Image Retrieval ◽  
Statistical Parameters ◽  
Query Image ◽  
Similar Images ◽  
Matching Score ◽  
The Cost ◽  
The Given

We present a framework that permits in classifying medical images so as to recognize conceivable diseases that affected. This is done by Image retrieval from the collection of dataset by inputting the query image. Content based Image retrieval (CBIR) is the way toward seeking comparable pictures from a picture database dependent on the visual substance of the given query image. Even though some studies present general method in image extraction, there are no efficient methods in medical image retrieval with accuracy. To overcome and to eliminate these flaws our proposed CBIR method examined with the accurate and efficient way for feature extraction from medical images. The images used are grey scale image. The dataset holds the n number of images related to medical particularly brain tumor images. To retrieve the related images from the dataset and get the corresponding details, image is given as an input i.e., query image. Initially, the query image is analyzed by shape, texture and histogram and the result obtained from this is compared with the similar images in dataset. The similarities between the images are found by implementing the Matching Score algorithm. This algorithm provides accuracy in matching the image that helps greatly at the time of classification. The results of computation is said to be the features for the given image. Also the cost for processing the image is comparatively low. The technique has been examined on standard image dataset and satisfactory results have been achieved

scholarly journals CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 294-299
Author(s):  
HARSHADA ANAND KHUTWAD ◽  
RAVINDRA JINADATTA VAIDYA
Keyword(s):  
Image Retrieval ◽  
Wavelet Decomposition ◽  
Texture Features ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Query Image ◽  
Shape Information ◽  
Color Correlogram ◽  
Similar Images ◽  
The Given

Content Based Image Retrieval is an interesting and most emerging field in the area of ‘Image Search’, finding similar images for the given query image from the image database. Current approaches include the use of color, texture and shape information. Considering these features in individual, most of the retrievals are poor in results and sometimes we are getting some non relevant images for the given query image. So, this dissertation proposes a method in which combination of color and texture features of the image is used to improve the retrieval results in terms of its accuracy. For color, color histogram based color correlogram technique and for texture wavelet decomposition technique is used. Color and texture based image

scholarly journals Image Retrieval Based on WBCH and Clustering Algorithm

2013 ◽  
Vol 5 (3) ◽  
pp. 604-613
Author(s):  
Asmita Bhaskar Shirsath ◽  
M. J. Chouhan ◽  
N. J Uke
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Similarity Measures ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Shape Information ◽  
Image Descriptors ◽  
Image Retrieval System ◽  
Retrieval Result

Research on content-based image retrieval has gained tremendous momentum during the last decade. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. In order to get faster  retrieval result from large-scale image database ,we proposed image retrieval system in which image database is first pre-processed by Wavelet Based Color Histogram (WBCH) and K-means algorithm and then using Hierarchical clustering algorithm we index the previous result and then by using similarity measures we retrieve the images from pre-processed database. Experiments show that this proposed method offers substantial increase in retrieval speed but needs to be improved on retrieval results.

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