Weighted feature fusion for content-based image retrieval

2016 ◽  
Author(s):  
Omurhan A. Soysal ◽  
Emre Sumer
Keyword(s):  
Image Retrieval ◽  
Feature Fusion ◽  
Content Based Image Retrieval

scholarly journals AN OPTIMIZED FEATURE EXTRACTION TECHNIQUE FOR CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 11-16
Author(s):  
SAVITHA SIVAN ◽  
THUSNAVIS BELLA MARY. I
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Fusion ◽  
Texture Features ◽  
Research Area ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Multimedia Technologies ◽  
Active Research ◽  
Occurrence Matrix

Content-based image retrieval (CBIR) is an active research area with the development of multimedia technologies and has become a source of exact and fast retrieval. The aim of CBIR is to search and retrieve images from a large database and find out the best match for the given query. Accuracy and efficiency for high dimensional datasets with enormous number of samples is a challenging arena. In this paper, Content Based Image Retrieval using various features such as color, shape, texture is made and a comparison is made among them. The performance of the retrieval system is evaluated depending upon the features extracted from an image. The performance was evaluated using precision and recall rates. Haralick texture features were analyzed at 0 o, 45 o, 90 o, 180 o using gray level co-occurrence matrix. Color feature extraction was done using color moments. Structured features and multiple feature fusion are two main technologies to ensure the retrieval accuracy in the system. GIST is considered as one of the main structured features. It was experimentally observed that combination of these techniques yielded superior performance than individual features. The results for the most efficient combination of techniques have also been presented and optimized for each class of query.

scholarly journals Feature Fusion for Image Retrieval using Image Processing

2019 ◽  
Vol 8 (12) ◽  
pp. 3157-3162
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Retrieval ◽  
Principle Component Analysis ◽  
Clustering Algorithm ◽  
Feature Fusion ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Huge Amount ◽  
Attribute Data

Image processing and computer vision uses Content-based image retrieval (CBIR) function to solve the issue of image retrieval, which means, solving the issue of image searching in expansive databases. The actual data of the image will be evaluated when a search is performed that refers to content-based. The term content can be any attribute of an image like colour-shade, various symbols or shapes, sizes, or any other data. There are various approaches for image retrieval but the most prominent are by comparing the main image with the subsets of the relatable images whether it matches or not and the other one is by using a matching descriptor for the image. One of the main trouble for huge amount of CBIR is the representation of an image. When a given image is worked upon it is divided into number of attributes in which some are the primary ones and others are the secondary ones. These attributes are checked with the local and MPEG-7 descriptors. All this is then mapped in a single vector which is the same images but in compact form to save the space. Principle Component Analysis (PCA) is used lessen the attribute size. To store the attribute data in similar clusters and to train them to give the correct output the study also uses k-means clustering algorithm. Hence, the proposed system deals with the image retrieval using various algorithms and methods.

Corrigendum to ‘Content-based image retrieval technology using multi-feature fusion’ [Optik 126 (2015) 2144–2148]

Optik ◽  
2016 ◽  
Vol 127 (12) ◽  
pp. 5170 ◽  
Author(s):  
Min Huang ◽  
Huazhong Shu ◽  
Yaqiong Ma ◽  
Qiuping Gong
Keyword(s):  
Image Retrieval ◽  
Feature Fusion ◽  
Content Based Image Retrieval

Content-based image retrieval technology using multi-feature fusion

Optik ◽  
2015 ◽  
Vol 126 (19) ◽  
pp. 2144-2148 ◽  
Author(s):  
Min Huang ◽  
Huazhong Shu ◽  
Yaqiong Ma ◽  
Qiuping Gong
Keyword(s):  
Image Retrieval ◽  
Feature Fusion ◽  
Content Based Image Retrieval

scholarly journals Framework for Color and Texture Feature Fusion in Content Based Image Retrieval using Block Truncation Coding with Color Spaces

2020 ◽  
Vol 9 (3) ◽  
pp. 769-774
Keyword(s):  
Image Retrieval ◽  
Feature Fusion ◽  
Texture Feature ◽  
Local Binary Patterns ◽  
Input Image ◽  
Content Based Image Retrieval ◽  
Color Spaces ◽  
Query Image ◽  
Block Truncation Coding ◽  
Rate Of Increase

With tremendous growth in social media and digital technologies, generation, storing and transfer of huge amount of information over the internet is on the rise. Images or visual mode of communication have been prevailing and widely accepted as a mode of communication since ages. And with the growth of internet, the rate at which images are generated is growing exponentially. But the methods used to retrieve images are still very slow and inefficient, compared to the rate of increase in image databases. To cope up with this explosive increase in images, this information age has seen huge research advancement in Content Based Image Retrieval (CBIR). CBIR systems provide a way of utilizing the 3 major ways in which content is portrayed in images, those are shape, texture and color. In CBIR system, features are extracted from query image and similarity is found with features stored in database for retrieval. This provides an objective way of image retrieval, which is more efficient compared to subjective human annotation. Application specific CBIR systems have been developed and perform really well, but Generic CBIR systems are still under developed. Block Truncation Coding (BTC) has been chosen as a feature extractor. BTC applied directly on input image provides color content-based features of image and BTC applied after applying LBP on the image provide texture content-based features of image. Previous work consists of either color, shape or texture, but usage of more than one descriptor is still in research and might give better performance. The paper presents framework for color and texture feature fusion in content-based image retrieval using block truncation coding with color spaces. Experimentation is carried out on Wang Dataset of 1000 images consisting of 10 classes. Each class has 100 images in it. Obtained results have shown performance improvement using fusion of BTC extracted color features and texture features extracted with BTC applied on Local Binary Patterns (LBP). Conversion of color space from RGB to LUV is done using Kekre's LUV.

A decisive content based image retrieval approach for feature fusion in visual and textual images

2019 ◽  
Vol 179 ◽  
pp. 8-20 ◽  
Author(s):  
Salahuddin Unar ◽  
Xingyuan Wang ◽  
Chunpeng Wang ◽  
Yu Wang
Keyword(s):  
Image Retrieval ◽  
Feature Fusion ◽  
Content Based Image Retrieval

scholarly journals Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

2021 ◽  
Vol 8 (1) ◽  
pp. 110-116
Author(s):  
Dannina Kishore ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Feature Fusion ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Single Feature ◽  
Image Retrieval System ◽  
Efficient Retrieval ◽  
Multiple Resolutions ◽  
Similar Images

In the last few years, Content-Based Image Retrieval (CBIR) has received wide attention. Compared to text-based image retrieval contents of the image are more in information for efficient retrieval by Content-Based Image Retrieval. The single feature cannot be applied to all the images and provides lower performance. In this paper, we have put forward a proposal on an image retrieval using multi-feature fusion. The concept of multi-resolution has been exploited with the help of a wavelet transform. This method combines Local Binary Pattern (LBP) with Fast and Accurate Exponent Fourier Moments (FAEFM’s) with the wavelet decomposition of an image using multiple resolutions. In order to extract the feature of texture from image, LBP codes of Discrete Wavelet Transform (DWT), the image coefficients are estimated followed by the computation of Fast and Accurate Exponent Fourier Moments to these LBP codes so as to extract features of shape to construct the required feature vector. These constructed vectors aid us in exactly finding out and retrieving visually similar images from existing databases. The benchmark databases Corel-1k and Olivia 2688 are used to test the proposed method. The proposed method achieves 99.99% of precision and 93.15% of recall on Corel-1k database and 99.99% of precision and recall of 93.63% on Olivia-2688 database, which are higher than the existing methods.

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