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 Hierarchical Image Retrieval by Multi-Feature Fusion

2017 ◽  
Author(s):  
Xiaojun Lu ◽  
Jiaojuan Wang ◽  
Yingqi Hou ◽  
Mei Yang ◽  
Qi Wang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Large Time ◽  
Retrieval System ◽  
Feature Fusion ◽  
Retrieval Method ◽  
Time Consumption ◽  
Retrieval Accuracy ◽  
Single Feature ◽  
Image Retrieval System ◽  
Accuracy Rates

Aiming at the problems that are poor generalization performance, low retrieval accuracy and large time consumption of existing content-based image retrieval system, the hierarchical image retrieval method based on multi feature fusion is proposed in this paper. The retrieval accuracy rates on Corel5K, UKbeach and Holidays are 68.23(Top 1), 3.73(N-S) and 88.20(mAp), respectively. The experimental results show that the method proposed in this paper can effectively improve the deficiency of single feature retrieval and save time significantly in the premise of a small amount of loss of accuracy.

scholarly journals An Adaptive Weight Method for Image Retrieval Based Multi-Feature Fusion

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 577 ◽  
Author(s):  
Xiaojun Lu ◽  
Jiaojuan Wang ◽  
Xiang Li ◽  
Mei Yang ◽  
Xiangde Zhang
Keyword(s):  
Image Retrieval ◽  
Transfer Matrix ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Feature Fusion ◽  
Rapid Development ◽  
Information Storage ◽  
Query Image ◽  
Retrieval Accuracy ◽  
Single Feature

With the rapid development of information storage technology and the spread of the Internet, large capacity image databases that contain different contents in the images are generated. It becomes imperative to establish an automatic and efficient image retrieval system. This paper proposes a novel adaptive weighting method based on entropy theory and relevance feedback. Firstly, we obtain single feature trust by relevance feedback (supervised) or entropy (unsupervised). Then, we construct a transfer matrix based on trust. Finally, based on the transfer matrix, we get the weight of single feature through several iterations. It has three outstanding advantages: (1) The retrieval system combines the performance of multiple features and has better retrieval accuracy and generalization ability than single feature retrieval system; (2) In each query, the weight of a single feature is updated dynamically with the query image, which makes the retrieval system make full use of the performance of several single features; (3) The method can be applied in two cases: supervised and unsupervised. The experimental results show that our method significantly outperforms the previous approaches. The top 20 retrieval accuracy is 97.09%, 92.85%, and 94.42% on the dataset of Wang, UC Merced Land Use, and RSSCN7, respectively. The Mean Average Precision is 88.45% on the dataset of Holidays.

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.

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.

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

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