scholarly journals Image Retrieval using Hybrid Order Dither Block Truncation Code

2021 ◽  
Vol 9 (VI) ◽  
pp. 2049-2054
Author(s):  
Sugamya Katta
Keyword(s):  
Image Retrieval ◽  
Data Stream ◽  
Retrieval System ◽  
Image Features ◽  
New Approach ◽  
Image Retrieval System ◽  
Indexing Technique ◽  
Compressed Data

A new approach is proposed to index images in database using features generated from the HODBTC compressed data stream. This indexing technique can be extended for CBIR. HODBTC compresses an image into a set of color quantizers and a bitmap image. The proposed image retrieval system generates two image features namely CCF and BPF from the minimum quantizer, maximum quantizer and bitmap image respectively by involving the visual codebook.

scholarly journals A Novel Adaptive Feature Fusion Strategy for Image Retrieval

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1670
Author(s):  
Xiaojun Lu ◽  
Libo Zhang ◽  
Lei Niu ◽  
Qing Chen ◽  
Jianping Wang
Keyword(s):  
Image Retrieval ◽  
Information Entropy ◽  
Retrieval System ◽  
Feature Fusion ◽  
Research Direction ◽  
Image Features ◽  
Retrieval Strategy ◽  
Fusion Image ◽  
Single Feature ◽  
Image Retrieval System

In the era of big data, it is challenging to efficiently retrieve the required images from the vast amount of data. Therefore, a content-based image retrieval system is an important research direction to address this problem. Furthermore, a multi-feature-based image retrieval system can compensate for the shortage of a single feature to a certain extent, which is essential for improving retrieval system performance. Feature selection and feature fusion strategies are critical in the study of multi-feature fusion image retrieval. This paper proposes a multi-feature fusion image retrieval strategy with adaptive features based on information entropy theory. Firstly, we extract the image features, construct the distance function to calculate the similarity using the information entropy proposed in this paper, and obtain the initial retrieval results. Then, we obtain the precision of single feature retrieval based on the correlation feedback as the retrieval trust and use the retrieval trust to select the effective features automatically. After that, we initialize the weights of selected features using the average weights, construct the probability transfer matrix, and use the PageRank algorithm to update the initialized feature weights to obtain the final weights. Finally, we calculate the comprehensive similarity based on the final weights and output the detection results. This has two advantages: (1) the proposed strategy uses multiple features for image retrieval, which has better performance and more substantial generalization than the retrieval strategy based on a single feature; (2) compared with the fixed-feature retrieval strategy, our method selects the best features for fusion in each query, which takes full advantages of each feature. The experimental results show that our proposed method outperforms other methods. In the datasets of Corel1k, UC Merced Land-Use, and RSSCN7, the top10 retrieval precision is 99.55%, 88.02%, and 88.28%, respectively. In the Holidays dataset, the mean average precision (mAP) was 92.46%.

scholarly journals Indexing Techniques in Trademark Image Retrieval Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 389-392
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Global Features ◽  
Retrieval Systems ◽  
Indexing Techniques ◽  
Image Retrieval System ◽  
Speed Up ◽  
Trademark Images ◽  
Clear Idea ◽  
Indexing Technique

The development of automatic trademark image retrieval systems becomes a necessity because of the increasing number of registered trademarks in all countries. The goal is to protect the registered trademarks from counterfeiting and infringement. This paper introduces a trademark image retrieval system using indexing techniques. The proposed system is described by giving an overview about its architecture and describing in details all its components. The goal is to allow researchers and developers in image retrieval to build their own trademark retrieval system using the indexing techniques. Each part of the proposed system is considered as a component that can be improved or replaced. The reader can have a clear idea on: (1) the type of visual features to extract from the trademark images, (2) the indexing technique that can be used to organize the extracted features and speed-up the search and (3) how to perform a similar search for a new trademark image. The proposed system has been evaluated using several global features and the best performance is obtained when using Zernike moments coefficients with order 12.

scholarly journals Convolutional Neural Networks in Image Retrieval System

2020 ◽  
Vol 9 (5) ◽  
pp. 123-129
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Retrieval ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Retrieval System ◽  
Prediction Models ◽  
Image Features ◽  
Medical Image Retrieval ◽  
Image Retrieval System

Image is an important medium for monitoring the treatment responses of patient’s diseases by the physicians. There could be a tough task to organize and retrieve images in structured manner with respect to incredible increase of images in Hospitals. Text based image retrieval may prone to human error and may have large deviation across different images. Content-Based Medical Image Retrieval(CBMIR) system plays a major role to retrieve the required images from the huge database.Recent advances in Deep Learning (DL) have made greater achievements for solving complex problems in computer vision ,graphics and image processing. The deep architecture of Convolutional Neural Networks (CNN) can combine the low-level features into high-level features which could learn the semantic representation from images. Deep learning can help to extract, select and classify image features, measure the predictive target and gives prediction models to assist physician efficiently. The motivation of this paper is to provide the analysis of medical image retrieval system using CNN algorithm.

A Multi-Label Image Annotation With Multi-Level Tagging System

2018 ◽  
pp. 28-47
Author(s):  
Kalaivani Anbarasan ◽  
Chitrakala S.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Annotation ◽  
Image Features ◽  
Semantic Gap ◽  
Low Level ◽  
Image Retrieval System ◽  
Multi Level ◽  
High Level ◽  
Tagging System

The content based image retrieval system retrieves relevant images based on image features. The lack of performance in the content based image retrieval system is due to the semantic gap. Image annotation is a solution to bridge the semantic gap between low-level content features and high-level semantic concepts Image annotation is defined as tagging images with a single or multiple keywords based on low-level image features. The major issue in building an effective annotation framework is the integration of both low level visual features and high-level textual information into an annotation model. This chapter focus on new statistical-based image annotation model towards semantic based image retrieval system. A multi-label image annotation with multi-level tagging system is introduced to annotate image regions with class labels and extract color, location and topological tags of segmented image regions. The proposed method produced encouraging results and the experimental results outperformed state-of-the-art methods

scholarly journals Analysis and Comparison of Color Features for Content Based Image Retrieval

2005 ◽  
Vol 4 (2) ◽  
pp. 522-527
Author(s):  
Milind Vijayrao Lande ◽  
Prof. PraveenBhanodiya ◽  
Mr. Pritesh Jain
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Experimental Comparison ◽  
Structural Color ◽  
Multidimensional Indexing ◽  
Color Features ◽  
Image Retrieval System ◽  
Different Color

Creation of a content-based image retrieval system implies solving a number of difficult problems, including analysis of low-level image features and construction of feature vectors, multidimensional indexing, design of user interface, and data visualization. Color is one of the important features used in CBIR systems. The methods of characterizing color fall into two major categories:  Histograms and Statistical. An experimental comparison of a number of different color features for content-based image retrieval presented in these paper. The primary goal is to determine which color feature is most efficient in representing the spatial distribution of images. In this paper, we analyze and evaluate both Statistical and Structural color features. For the experiments, publicly available image databases are used. Analysis and comparison of individual color features are presented

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