Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional Weber Patterns

2021 ◽  
pp. 2150046
Author(s):  
Gangavarapu Venkata Satya Kumar ◽  
Pillutla Gopala Krishna Mohan
Keyword(s):  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
The Novel ◽  
Image Content ◽  
Query Image ◽  
Proposed Model ◽  
Maximum Mutual Information ◽  
Basic Features

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image’s basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.

A Content Based Image Retrieval Model Using Feature Space Dividing

2014 ◽  
Vol 596 ◽  
pp. 388-393
Author(s):  
Guan Huang
Keyword(s):  
Image Retrieval ◽  
Fractional Order ◽  
Feature Space ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Time Cost ◽  
Retrieval Model ◽  
Matching Accuracy ◽  
Query Image ◽  
Proposed Model

This paper introduces a model for content based image retrieval. The proposed model extracts image color, texture and shape as feature vectors; and then the image feature space is divided into a group of search zones; during the image searching phase, the fractional order distance is utilized to evaluate the similarity between images. As the query image vector only needs to compare with library image vectors located in the same search zone, the time cost is largely reduced. Further more the fractional order distance is utilized to improve the vector matching accuracy. The experimental results demonstrated that the proposed model provides more accurate retrieval results with less time cost compared with other methods.

A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

2019 ◽  
pp. 54-64
Author(s):  
Mohamed Elsharkawy ◽  
◽  
Ahmed N. Al Masri ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Image Encryption ◽  
Cuckoo Search ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Search Optimization ◽  
Proposed Model ◽  
Cuckoo Search Optimization ◽  
Visual Properties

From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over 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 Implementation of early and late fusion methods for content-based image retrieval

2021 ◽  
Vol 8 (7) ◽  
pp. 97-105
Author(s):  
Ali Ahmed ◽  
◽  
Sara Mohamed ◽  
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Late Fusion ◽  
Query Image ◽  
Fusion Methods ◽  
And Performance ◽  
Image Repository

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

Machine Learning Approach for Content Based Image Retrieval

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Learning Approach ◽  
Query Image ◽  
Low Level ◽  
Machine Learning Approach ◽  
Efficient Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

Content-based image retrieval based on supervised learning and statistical-based moments

2019 ◽  
Vol 33 (19) ◽  
pp. 1950213 ◽  
Author(s):  
Vibhav Prakash Singh ◽  
Rajeev Srivastava ◽  
Yadunath Pathak ◽  
Shailendra Tiwari ◽  
Kuldeep Kaur
Keyword(s):  
Image Retrieval ◽  
Supervised Learning ◽  
Texture Features ◽  
Ground Truth ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approaches ◽  
Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION

2003 ◽  
Vol 03 (01) ◽  
pp. 119-143 ◽  
Author(s):  
ZHIYONG WANG ◽  
ZHERU CHI ◽  
DAGAN FENG ◽  
AH CHUNG TSOI
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Network Model ◽  
Neural Network Model ◽  
Relevance Feedback ◽  
Image Representation ◽  
Tree Structure ◽  
Content Based Image Retrieval ◽  
Adaptive Processing ◽  
Image Content

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.

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