scholarly journals Feature Extraction and Image Retrieval of Landscape Images Based on Image Processing

2020 ◽  
Vol 37 (6) ◽  
pp. 1009-1018
Author(s):  
Zhe Li ◽  
Xiao Han ◽  
Liya Wang ◽  
Tongyi Zhu ◽  
Futian Yuan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Color Space ◽  
Color Quantization ◽  
Image Libraries ◽  
Color System ◽  
Space Pattern ◽  
Rational Management ◽  
Novel Method

Facing the existing digital image libraries on landscape, researchers need to urgently solve a challenging problem: how to realize rational management and accurate retrieval of landscape images that contain feature information like hierarchy, layout, color system, and color matching. For accurate organization and labeling of landscape Images, this paper presents a novel method for feature extraction and image retrieval of landscape images based on image processing. Firstly, a color quantization process was designed for landscape images, and used to analyze the color composition and color space pattern (CSP) of such images. Next, the existing methods, which are suitable for the extraction of color features from landscape Images, were briefly reviewed, and the basic flows of our improved algorithm and division method of landscape color blocks (LCBs) were explained. Finally, the retrieval performance of landscape images was improved by matching of weighted color blocks of regional landscape, based on the multi-dimensional color eigenvectors of landscape image. The experimental results demonstrate the effectiveness of our algorithm. The research results shed light on the feature extraction from other types of color images.

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.

Radial Moments for Image Retrieval

2017 ◽  
pp. 358-389
Author(s):  
Pooja Sharma
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Performance Measures ◽  
Similarity Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Visual Data ◽  
Image Feature Extraction ◽  
Radial Moments

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

Image retrieval of wool fabric. Part II: based on low-level color features

2019 ◽  
Vol 90 (7-8) ◽  
pp. 797-808
Author(s):  
Ning Zhang ◽  
Jun Xiang ◽  
Lei Wang ◽  
Nian Xiong ◽  
Weidong Gao ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Human Vision ◽  
Computational Time ◽  
Color Measurement ◽  
Wool Fabric ◽  
Color Quantization ◽  
Image Partition ◽  
Measure Methods ◽  
Low Efficiency

Color is difficult to distinguish by human vision and is described by keywords, resulting in low efficiency of wool fabric retrieval in factories at present. To obtain the process sheets of existing products and reduce the work of color measurement in sample analysis, this paper proposes an effective method based on dominant colors (DCs) and color moments (CMs) for wool fabric image retrieval. Firstly, the image was scaled to reduce computational time. Then, the hue, saturation, value color space was divided into 128 parts by the fast color quantization algorithm to extract the DCs of the image. Meanwhile, the CMs based on image partition were calculated in CIE L* a* b* color space to describe the spatial color information. Subsequently, different similarity measure methods were carried out based on the DC feature and CM feature. Finally, experiments were conducted on a wool fabric image database with 20,000 images for parameter optimization and verification. The average precision and recall were up to 87% and 44%, respectively. Experimental results show that the proposed scheme can retrieve images with the same or similar colors quickly and effectively and it outperformed other methods, providing referential assistance for the factory worker when retrieving wool fabrics.

scholarly journals Content Based Image Retrieval using Feature Extraction Technique

2019 ◽  
Vol 9 (2) ◽  
pp. 2527-2532
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Recall Rate ◽  
Distance Metrics ◽  
Average Precision ◽  
Feature Extraction Method ◽  
Retrieval Technique ◽  
Precision Rate ◽  
Feature Extraction Technique

Now days the image processing can be used in various areas such as in Agriculture, in Health care system also for security purpose. In case of Crime investigation the image processing can be used to identify the particular suspect from an available dataset for that purpose an image retrieval technique is presented in this paper. For image retrieval number of techniques is available. In earlier days Block Truncation Coding is used but due its some disadvantage feature extraction method is used. Using DDBTC technique two features are derived. The first feature as Color Co-occurrence Features (CCF) obtained using color quantizes features such as Bit Pattern Feature (BPF) is derived from Bitmap image. The five different distance metrics are used to measure the similarity between two images. The simulated results shows proposed Technique can shows the better result in the form of Average Precision rate (APR) and Average Recall Rate (ARR) as compared to other techniques.

scholarly journals Design of Feature Extraction in Content Based Image Retrieval (CBIR) using Color and Texture

2012 ◽  
pp. 157-161
Author(s):  
Swati V. Sakhare ◽  
Vrushali G. Nasre
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Image Database ◽  
Color Histogram ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Efficient Approach ◽  
Novel Method ◽  
Fuzzy Color Histogram ◽  
Color Texture

Retrieval of images based on visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. Various techniques have been implemented using these features like fuzzy color histogram, Tammura texture etc. In this paper we propose a novel method with highly accurate and retrieval efficient approach which will work on large image database with varied contents and background.

Radial Moments for Image Retrieval

2018 ◽  
pp. 2420-2451
Author(s):  
Pooja Sharma
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Performance Measures ◽  
Similarity Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Visual Data ◽  
Image Feature Extraction ◽  
Radial Moments

Images have always been considered an effective medium for presenting visual data in numerous applications ranging from industry to academia. Consequently, managing and indexing of images become essential in order to retrieve relevant images effectively and efficiently. Therefore, the proposed chapter aims to elaborate one of the advanced concepts of image processing, i.e., Content Based Image Retrieval (CBIR) and image feature extraction using advanced methods known as radial moments. In this chapter, various radial moments are discussed with their properties. Besides, performance measures and various similarity measures are elaborated in depth. The performance of radial moments is evaluated through an extensive set of experiments on benchmark databases such as Kimia-99, MPEG-7, COIL-100, etc.

scholarly journals A Novel Method for Feature Extraction using Color Layout Descriptor (CLD) and Edge histogram Descriptor (EHD)

2020 ◽  
Vol 9 (4) ◽  
pp. 2147-2150
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Development Process ◽  
Extraction Techniques ◽  
Processing Application ◽  
Image Processing Application ◽  
Novel Method ◽  
Low Efficiency ◽  
Almost All ◽  
Edge Histogram

Now days, Image processing finds diversified applications in almost all field of life. The success of any image processing application is depends on proper feature extraction technique. To extract good and proper features is very interesting and challenging task in the development process. It is used to describe the image based on its contents. These extracted features are used to compare, analyse and/or search the analogous images. There are various feature extraction techniques are found in the literature to design various applications. However any image processing application generates images with high dimensionality, which will be results in the low efficiency of an application. This paper provides an approach to extract features from the images using MPEG-7 feature extraction techniques. The approach discussed in the paper uses two popular MPEG-7 visual content descriptors; they are namely Edge Histogram Descriptor (EHD) and Color Layout Descriptor (CLD). The concept results in reduction of dimensions of an image to improve the efficiency of the application. It can be used as a heart to design any image processing application as well as provides strong foundation to develop variety of applications.

scholarly journals CBIR : Color Feature Extraction using CIELAB Color Space with Compact Color Signature

2020 ◽  
Vol 9 (6) ◽  
pp. 470-476
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
New Technology ◽  
Color Space ◽  
Digital Camera ◽  
Input Image ◽  
Content Based Image Retrieval ◽  
Color Representation ◽  
Cielab Color Space ◽  
Cielab Color

The immense progress of new technology we have been created an enormous number of digital images by using such devices as a digital camera, scanner, and mobile phones so on. All the images which are taken by the devices to keep in Image Database. For retrieving the desire images which were given in an input image has compared with the large database according to the visual content used by the technique as referred to as the Content Based Image Retrieval (CBIR) system. There are two phases for retrieving images in the CBIR system, as the first one is feature extraction and the second one is similarity size. Thus, the feature extraction consists of every image has produced symbolic content in the form of the function. The visual contents of an image in the CBIR system contain the features which have represented as shape, texture, spatial region and color of the images. In our paper tries to design the images’ color features as in the steps to focus color representation in the k-d tree, CIELAB color space of color signature compression along with categories of Human’s color for Content-based image retrieval and also acquire the results using MATLAB.

scholarly journals A perception-based color space for illumination-invariant image processing

2008 ◽  
Vol 27 (3) ◽  
pp. 1-7 ◽  
Author(s):  
Hamilton Y. Chong ◽  
Steven J. Gortler ◽  
Todd Zickler
Keyword(s):  
Image Processing ◽  
Color Space ◽  
Illumination Invariant
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