scholarly journals Color Based Image Retrieval by Combining Various Features

2019 ◽  
Vol 9 (2) ◽  
pp. 454-460
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Semantic Features ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Mathematical Formulas ◽  
Image Set ◽  
Different Color ◽  
Image Collection

Content based image retrieval system retrieve the images according to the strong feature related to desire as color, texture and shape of an image. Although visual features cannot be completely determined by semantic features, but still semantic features can be integrate easily into mathematical formulas. This paper is focused on retrieval of images within a large image collection, based on color projection by applying segmentation and quantification on different color models and compared for good result. This method is applied on different categories of image set and evaluated its retrieval rate in different models

Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

2020 ◽  
Vol 10 (1) ◽  
pp. 1-23
Author(s):  
Maher Alrahhal ◽  
Supreethi K.P.
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Retrieval System ◽  
Similarity Measures ◽  
Visual Features ◽  
Classification Problems ◽  
Bag Of Features ◽  
Image Retrieval System ◽  
Multimedia Image ◽  
Medical Dataset

The authors propose WNAHVF, a combined weighted and normalized AlexNet with handcrafted visual features for extracting features from images and using those vectors for image retrieval and classification. The authors test the WNAHVF method on two general datasets, Corel-1k and Corel-10k, and one medical dataset. The outcomes demonstrate combining Bag of Features and Local Neighbor patterns with AlexNet enhances the accuracy and gives better results in general and medical image datasets in retrieval and classification problems. This algorithm gives results that are superior to existing strategies.

scholarly journals A Knowledge-based Image Retrieval System Integrating Semantic and Visual Features

2016 ◽  
Vol 96 ◽  
pp. 1428-1436 ◽  
Author(s):  
Olfa Allani ◽  
Hajer Baazaoui Zghal ◽  
Nedra Mellouli ◽  
Herman Akdag
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Knowledge Based ◽  
Image Retrieval System

Pattern graph-based image retrieval system combining semantic and visual features

2017 ◽  
Vol 76 (19) ◽  
pp. 20287-20316
Author(s):  
Olfa Allani ◽  
Hajer Baazaoui Zghal ◽  
Nedra Mellouli ◽  
Herman Akdag
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Image Retrieval System ◽  
Pattern Graph

Semi-Subsampled Contourlet Retrieval Algorithm Using Three Statistical Features

2012 ◽  
Vol 433-440 ◽  
pp. 3117-3123
Author(s):  
Yu Xi Liu ◽  
Xin Wu Chen
Keyword(s):  
Standard Deviation ◽  
Image Retrieval ◽  
Retrieval System ◽  
System Structure ◽  
Contourlet Transform ◽  
Retrieval Algorithm ◽  
Feature Vectors ◽  
Retrieval Rate ◽  
Decomposition Structure ◽  
Image Retrieval System

In order to improve the retrieval rate of contourlet transform retrieval system, a semi-subsampled contourlet transform based texture image retrieval system was proposed. In the system, the contourlet transform was constructed by non-subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks, sub-bands standard deviation, absolute mean energy and kurtosis in semi-subsampled contourlet domain are cascaded to form feature vectors, and the similarity metric is Canberra distance. Experimental results on 109 brodatz texture images show that using the three cascaded features can lead to a higher retrieval rate than the combination of standard deviation and absolute mean which is most commonly used today under same dimension of feature vectors. Semi-subsampled contourlet transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet system under the same system structure with same length of feature vectors, retrieval time and memory needed, decomposition structure parameters can also make significant effects on retrieval rates, especially scale number.

scholarly journals Analysis of combined approaches of CBIR systems by clustering at varying precision levels

2021 ◽  
Vol 11 (6) ◽  
pp. 5009
Author(s):  
S. M. Zakariya ◽  
Imtiaz A. Khan
Keyword(s):  
Image Retrieval ◽  
Threshold Value ◽  
Visual Features ◽  
Texture Model ◽  
Retrieval Technique ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
The Individual ◽  
The Given ◽  
Color Texture

<span lang="EN-US">The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and color-texture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named cluster-based retrieval of images by unsupervised learning (CLUE).</span>

scholarly journals CONTENT BASED IMAGE RETRIEVAL SYSTEM BY FUSION OF COLOR, TEXTURE AND EDGE FEATURES WITH SVM CLASSIFIER AND RELEVANCE FEEDBACK

2018 ◽  
Vol 6 (9) ◽  
pp. 259-273
Author(s):  
Priyanka Saxena ◽  
Shefali
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Image Retrieval System ◽  
Combined Features ◽  
Edge Features ◽  
Color Texture

Content Based Image Retrieval system automatically retrieves the most relevant images to the query image by extracting the visual features instead of keywords from images. Over the years, several researches have been conducted in this field but the system still faces the challenge of semantic gap and subjectivity of human perception. This paper proposes the extraction of low-level visual features by employing color moment, Local Binary Pattern and Canny Edge Detection techniques for extracting color, texture and edge features respectively. The combination of these features is used in conjunction with Support Vector Machine to reduce the retrieval time and improve the overall precision. Also, the challenge of semantic gap between low and high level features is addressed by incorporating Relevance Feedback. Average precision value of 0.782 was obtained by combining the color, texture and edge features, 0.896 was obtained by using combined features with SVM, 0.882 was obtained by using combined features with Relevance Feedback to overcome the challenge of semantic gap. Experimental results exhibit improved performance than other state of the art techniques.

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.

An Express Retrieval Method of Face Based on Semantic Features

2013 ◽  
Vol 850-851 ◽  
pp. 905-908
Author(s):  
Ming Zhang ◽  
Hai Wei Mu ◽  
Xiang Lou Liu ◽  
Dong Yan Zhao
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Recognition Rate ◽  
Face Image ◽  
Image Process ◽  
Semantic Features ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Image Retrieval System ◽  
Face Pattern

The paper uses digital image processing technology, technology of face pattern recognition and traditional database retrieval technology, integrate image retrieval technology based on version and content, and avoid the complexity of matching image process. The experiments with 200 human samples, correctly retrieved for 155 people, exactly matched to 125. The recognition rate of the system is 75.55%, the average time of search is less than 0.1s. Experiments indicate this method has strong robustness. The semantic face image retrieval system using this method has the characteristics of fast, efficient, practical.

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