Efficient image retrieval with multiple distance measures

1997 ◽  
Author(s):  
Andrew P. Berman ◽  
Linda G. Shapiro
Keyword(s):  
Image Retrieval ◽  
Distance Measures

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.

scholarly journals A simple multi-feature based stereoscopic medical image retrieval system

2019 ◽  
Vol 25 (2) ◽  
pp. 127-130
Author(s):  
K.A. Shaheer Abubacker ◽  
J. Sutha ◽  
K.A. Shahul Hameed
Keyword(s):  
Image Retrieval ◽  
Distance Measure ◽  
Performance Outcomes ◽  
Stereo Image ◽  
Distance Measures ◽  
Absolute Difference ◽  
Disparity Map ◽  
Query Image ◽  
Retrieval Systems ◽  
Feature Based

Abstract This paper describes a method of retrieving stereoscopic medical images from the database that consists of feature extraction, similarity measure, and re-ranking of retrieved images. This method retrieves similar images of the query image from the database and re-ranks them according to the disparity map. The performance is evaluated using the metrics namely average retrieval precision (APR) and average retrieval rate (ARR). According to the performance outcomes, the multi-feature based image retrieval using Mahalanobis distance measure has produced better result compared to other distance measures namely Euclidean, Minkowski, the sum of absolute difference (SAD) and the sum of squared absolute difference (SSAD). Therefore, the stereo image retrieval systems presented has high potential in biomedical image storage and retrieval systems.

scholarly journals Content Based Image Retrieval using Transform Domain Features and Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 3008-3011
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Integer Wavelet ◽  
Mean Variance ◽  
Efficient Recognition

In this paper, Content Based Image Retrieval using Transform domain features and algorithms has been implemented. The image can be decomposed by Discrete Wavelet Transform (DWT) to extract the features based on DC coefficients. Each sub-image is calculated by mean, variance and standard deviation to get more efficient recognition. The database image also applied in the domain of Stationary Wavelet Transform (SWT) and Integer Wavelet Transform (IWT) by using different distance measures. The proposed algorithm is the combination of DWT, SWT and IWT has been implemented using COREL database. This proposed method has more efficient recognition and less computational time over existing methods

scholarly journals A Study on Image Retrieval Based on Tetrolet Transform

2018 ◽  
Vol 7 (3.27) ◽  
pp. 321
Author(s):  
Devika Sarath ◽  
M Sucharitha
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Computation Time ◽  
Extraction Methods ◽  
Distance Measures ◽  
Computational Time ◽  
Multimedia Database ◽  
Texture Image ◽  
Survey Paper

Retrieving images from the large databases has always been one challenging problem in the area of image retrieval while maintaining the higher accuracy and lower computational time. Texture defines the roughness of a surface. For the last two decades due to the large extent of multimedia database, image retrieval has been a hot issue in image processing. Texture images are retrieved in a variety of ways. This paper presents a survey on various texture image retrieval methods. It provides a brief comparison of various texture image retrieval methods on the basis of retrieval accuracy and computation time. Image retrieval techniques vary with feature extraction methods and various distance measures. In this paper, we present a survey on various texture feature extraction methods by applying tertrolet transform. This survey paper facilitates the researchers with background of progress of image retrieval methods that will help researchers in the area to select the best method for texture image retrieval appropriate to their requirements.

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