Content Based Image Retrieval Using Wavelet Moments and Local Binary Patterns in CIE-L*a*b* Color Space

2018 ◽  
Author(s):  
Leila BOUSSAAD
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval

Content-based image retrieval based on supervised learning and statistical-based moments

2019 ◽  
Vol 33 (19) ◽  
pp. 1950213 ◽  
Author(s):  
Vibhav Prakash Singh ◽  
Rajeev Srivastava ◽  
Yadunath Pathak ◽  
Shailendra Tiwari ◽  
Kuldeep Kaur
Keyword(s):  
Image Retrieval ◽  
Supervised Learning ◽  
Texture Features ◽  
Ground Truth ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approaches ◽  
Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

2016 ◽  
Vol 25 (9) ◽  
pp. 4018-4032 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Satish Kumar Singh ◽  
Rajat Kumar Singh
Keyword(s):  
Image Retrieval ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval

scholarly journals Content-Based Image Retrieval using Color Quantization and Angle Representation

2014 ◽  
Vol 13 (10) ◽  
pp. 5094-5104
Author(s):  
Ihab Zaqout
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Measurement Methods ◽  
Content Based Image Retrieval ◽  
Similarity Measurement ◽  
Color Quantization ◽  
Human Visual Perception ◽  
Hsv Color Space ◽  
Perception System ◽  
Similar Images

An efficient non-uniform color quantization and similarity measurement methods are proposed to enhance the content-based image retrieval (CBIR) applications. The HSV color space is selected because it is close to human visual perception system, and a non-uniform color method is proposed to quantize an image into 37 colors. The marker histogram (MH) vector of size 296 values is generated by segmenting the quantized image into 8 regions (multiplication of 45°) and count the occurrences of the quantized colors in their particular angles. To cope with rotated images, an incremental displacement to the MH is applied 7 times. To find similar images, we proposed a new similarity measurement and other 4 existing metrics. A uniform color quantization of related work is implemented too and compared to our quantization method. One-hundred test images are selected from the Corel-1000 images database. Our experimental results conclude high retrieving precision ratios compared to other techniques.

Study on Image Retrieval Method Based on Color and Location

2012 ◽  
Vol 468-471 ◽  
pp. 2473-2477
Author(s):  
Lai Tang Ji ◽  
Tian Huang Chen
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Mobile Platform ◽  
Content Based Image Retrieval ◽  
Location Based Service ◽  
Histogram Method ◽  
Retrieval Method ◽  
Color Characteristics ◽  
Geometrical Character ◽  
Strong Robustness

In content-based image retrieval technology,color has been widely used as a kind of important image visual information.Compared with the geometrical character of image, color has certain stability and strong robustness to zoom, parallel move and rotate.Currently there are a lot of image retrieval technologies which own their own limitations.This paper puts forward an image retrieval method based on color and location,which is available for mobile platform.This paper adopts a histogram method in color characteristics and uses block color method to solve the color space distribution.Combined with the location-based service(LBS),this method can effectively improve the performance of image retrieval on the mobile platform.

Content Based Image Retrieval with Color Invariants

2013 ◽  
Vol 760-762 ◽  
pp. 1604-1608
Author(s):  
Ya Hui Song ◽  
Xiao Chen ◽  
Shan Shan Qu
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Markov Chain Model ◽  
Color Space ◽  
Content Based Image Retrieval ◽  
Chain Model ◽  
Feature Maps ◽  
Adaptive Clustering ◽  
Image Descriptors ◽  
Spatial Features

Content based image retrieval (CBIR) is an essential task in many applications. Color based methods have received much attention in past years, since color could serve efficiently for image retrieval, especially in the case of large database. However, there are two main drawbacks for color based image retrieval methods. Firstly, color based methods are not suitable for similar scenes under different illumination conditions, because color is sensitive to illumination. Secondly, existing approaches usually employ image descriptors with large size, which makes the approach unsuitable for real-time application. To overcome drawbacks mentioned above, an adaptive image retrieval method has been proposed, which integrates the color invariant with the spatial information about images. Different from previous methods, the quantization of the color space has not been manually determined. Instead, it has been decided according to the content of image, using an adaptive clustering technique. Therefore, the size of image descriptor is very small. In the proposed method, feature maps for images have been firstly established, which consist of color invariants. And then the Markov chain model has been employed to capture color information and spatial features. Finally, similar images are retrieved based on two-stage weighted distance. Experimental results show that the proposed method has improved simplicity and compactness of color based image retrieval methods, without the loss of efficiency and robustness.

scholarly journals Image Retrieval using Equalized Histogram Image Bins Moments

2012 ◽  
pp. 25-28
Author(s):  
N. K. Kamila ◽  
Pradeep Kumar Mallick ◽  
Sasmita Parida ◽  
B. Das
Keyword(s):  
Standard Deviation ◽  
Image Retrieval ◽  
Feature Vector ◽  
Color Space ◽  
Content Based Image Retrieval ◽  
Blue Color ◽  
Back Projection ◽  
Image Content ◽  
Meta Data ◽  
Histogram Bins

Content Based Image Retrieval (CBIR) operates on a totally different principle from keyword indexing. Primitive features characterizing image content, such as color, texture, and shape are computed for both stored and query images, and used to identify the images most closely matching the query. There have been many approaches to decide and extract the features of images in the database. Towards this goal we propose a technique by which the color content of images is automatically extracted to form a class of meta-data that is easily indexed. The color indexing algorithm uses the back-projection of binary color sets to extract color regions from images. This technique uses equalized histogram image bins of red, green and blue color. The feature vector is composed of mean, standard deviation and variance of 16 histogram bins of each color space. The new proposed methods are tested on the database of 600 images and the results are in the form of precision and recall.

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.

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