scholarly journals CBIR Using Slant Transform Using DC & AC Coefficients

2018 ◽  
Vol 7 (3.6) ◽  
pp. 276 ◽  
Author(s):  
N Sravani ◽  
K Veera Swamy
Keyword(s):  
Data Base ◽  
Feature Vector ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Vector Data ◽  
Query Image ◽  
Retrieval Technique ◽  
Slant Transform ◽  
Measuring Techniques ◽  
The Difference

In the CBIR- (Content Based Image Retrieval) technique requires low-level or primitive features- color, texture, or  other data that can be taken from its image Extracting feature vectors of database images as well as query image can be calculated with the help of slant transform by considering DC & 3 AC coefficients obtained in each block of an image. Slant transform represents the gradual brightness changes in an image line effectively. By calculating the difference between feature vector data base and feature vector for a query by using the distance measuring techniques. The vector of the smaller distance is the closest to query image. The experiment is performed in the Corel 500 Image Database. Finally, CBIR results are evaluated by the recall, precision, and F-Score.  

scholarly journals An Novel Framework For Content Based Image Retrieval With Quality Assessment System using Optimal Deep Convolution Neural Network

2019 ◽  
Vol 8 (12) ◽  
pp. 3916-3926
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Vector ◽  
Structural Similarity ◽  
Extraction Process ◽  
Input Image ◽  
Assessment System ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Occurrence Matrix

Content based image retrieval (CBIR) models become popular for retrieving images connected to the query image (QI) from massive dataset. Feature extraction process in CBIR plays a vital role as it affects the system’s performance. This paper is focused on the design of deep learning (DL) model for feature extraction based CBIR model. The presented model utilizes a ResNet50 with co-occurrence matrix (RCM) model for CBIR. Here, the ResNet50 model is applied for feature extraction of the QI. Then, the extracted features are placed in the feature repository as a feature vector. The RCM model computes the feature vector for every input image and compares it with the features present in the repository. Then, the images with maximum resemblance will be retrieved from the dataset. In addition, the resemblance between the feature vectors is determined by the use of co-occurrence matrix subtraction process. Besides, structural similarity (SSIM) measure is applied for the validation of the similarity among the images. A comprehensive results analysis takes place by the use of Corel 10K dataset. The experimental outcome indicated the superiority of the RCM model with respect to precision, recall and SSIM.

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>

Unsupervised defect detection algorithm for printed fabrics using content-based image retrieval techniques

2021 ◽  
pp. 004051752110086
Author(s):  
Xudong Hu ◽  
Mingyue Fu ◽  
Zhijuan Zhu ◽  
Zhong Xiang ◽  
Miao Qian ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Defect Detection ◽  
Image Database ◽  
Detection Algorithm ◽  
Content Based Image Retrieval ◽  
Reference Image ◽  
Difference Map ◽  
Free Sample ◽  
Effectiveness And Efficiency ◽  
The Difference

Automatic detection of printing defect technology is significant for improving printing fabrics’ appearance and quality. In this research, we proposed an unsupervised printing defect detection method by processing the difference map between the test image and the reference image. Aimed at this, we adopted a content-based image retrieval (CBIR) method to retrieve the reference image, which includes an image database, a convolutional denoising auto-encoder (CDAE) and a hash encoder (HE): the elements of image database are extracted from only one defect-free sample image of the test fabric; the CDAE prevents the system being affected by the texture of the fabric and provides a reliable feature description of the patterns; the HE indexes the feature vectors to binary code while maintaining their similarity; both CDAE and HE are trained in an unsupervised manner. With the retrieved reference image, the defect is determined by applying the Tsallis entropy thresholding and opening operation on the difference map. The method can be implemented without labeled and defective samples, and without consideration of the periodical primitive of patterns. Experimental results demonstrate the effectiveness and efficiency of the proposed method in defect detection for printed fabrics with complex patterns.

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 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 Improved Content-Based Image Retrieval Technique for Query Generation in Mobile Networks

2020 ◽  
Vol 9 (6) ◽  
pp. 526-530
Keyword(s):  
Image Retrieval ◽  
Mobile Networks ◽  
Content Based Image Retrieval ◽  
Multimedia Technology ◽  
Database Searching ◽  
Query Image ◽  
Retrieval Technique ◽  
Accuracy And Precision ◽  
Query Generation ◽  
A New Technique

Image database searching is in rapid growth with an advancement in multimedia technology. To manage these kinds of searches Content-Based Image Retrieval is an effective tool. In this paper, existing CBIR techniques are analyzed and a new technique has been proposed which works based on Region-Based Convolutional Neural Network (RCNN). In the proposed approach first of all image dataset is uploaded to cloud and features are stored in a storage. Then Query image is enhanced, uploaded and features are extracted. After this feature set is compared with dataset and matched images are extracted and ranked as the closest match. Using this proposed methodology, the accuracy and precision values are compared and validated and it is observed that the proposed methodology shows better results than the existing techniques.

Content-Based Image Retrieval Using Shape Features

2018 ◽  
pp. 39-61
Keyword(s):  
Image Retrieval ◽  
Data Base ◽  
Feature Vector ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Geometric Features ◽  
Shape Features ◽  
Base Object

This chapter presents CBIR methodologies for extracting geometric and margin features of objects in images and constructed as feature vector. This approach is unique in nature as the size of the feature is relatively small and capable of discriminating the query object with the data base object. These geometric features measure the object characteristics in terms of its shape and margin. Manhattan distance is used for measuring the similarity between query images and the database images for retrieving relevant images from the database.

scholarly journals An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

2021 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Shalaw Faraj Salih ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Retrieval ◽  
Communication Technologies ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Retrieval Technique ◽  
Current State ◽  
Similar Images ◽  
The Impact ◽  
Precision Rate ◽  
Color Texture

Applications for retrieving similar images from a large collection of images have increased significantly in various fields with the rapid advancement of digital communication technologies and exponential evolution in the usage of the Internet. Content-based image retrieval (CBIR) is a technique to find similar images on the basis of extracting the visual features such as color, texture, and/or shape from the images themselves. During the retrieval process, features and descriptors of the query image are compared to those of the images in the database to rank each indexed image accordingly to its distance to the query image. This paper has developed a new CBIR technique which entails two layers, called bi-layers. In the first layer, all images in the database are compared to the query image based on the bag of features (BoF) technique, and hence, the M most similar images to the query image are retrieved. In the second layer, the M images obtained from the first layer are compared to the query image based on the color, texture, and shape features to retrieve the N number of the most similar images to the query image. The proposed technique has been evaluated using a well-known dataset of images called Corel-1K. The obtained results revealed the impact of exploring the idea of bi-layers in improving the precision rate in comparison to the current state-of-the-art techniques in which achieved precision rate of 82.27% and 76.13% for top-10 and top-20, respectively.

Evaluation of Content Based Image Retrieval Technique Using an Efficient Hybrid Model

2020 ◽  
Vol 17 (4) ◽  
pp. 1885-1888
Author(s):  
M. A. Muthiah ◽  
N. Mathan ◽  
E. Logashanmugam
Keyword(s):  
Image Retrieval ◽  
Hybrid Model ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Shape Features ◽  
Query Image ◽  
Visual Technology ◽  
Retrieval Technique ◽  
Future Work ◽  
Calculated Distance

Due to vast enhancement in the field of visual technology, there are various sets of images. In order to reduce the complexity in retrieval of relevant images CBIR (Content Based Image Retrieval) technique can be used. CBIR using only color feature does not result in required output. So in this paper we introduced the concept of hybrid model which deals with color, texture along with shape features which gives an efficient output. A set of images are used to test the accuracy and the precision of each methods. Using Euclidean distance and Manhattan distance, similarity between query image and all the other images in database are calculated. Then the calculated distance values are arranged in ascending order. Based on this required images are retrieved. Experiment results shows that Hybrid model method had high accuracy and precise output compared to Color Histogram. Future work will be made to add one more feature (shape features) in order to get better results.

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