scholarly journals Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

2022 ◽  
Vol 9 (1) ◽  
pp. 138-147
Author(s):  
Mamat et al. ◽  
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Region Of Interest ◽  
Color Model ◽  
Retrieval Performance ◽  
Global Features ◽  
Hsv Color Model ◽  
High Level ◽  
Cie Lab ◽  
Large Image Databases

Content-based image retrieval involves the extraction of global feature images for their retrieval performance in large image databases. Extraction of global features image cause problem of the semantic gap between the high-level meaning and low-level visual features images. In this study RBIR, Region of Interest Based (ROI) Image Retrieval Using Incremental Frame of Color Image was proposed. It combines several methods, including filtering process, image partitioning using clustering and incremental frame formation, complementation law of theory set to generate ROI, NROI, or ER of the region. The concept of weighting as well as a significant query is also incorporated as a query strategy. Extensive experiments were also conducted on the Wang database and the color model selected was the CIE lab. Experimental results show the proposed method is efficient in image retrieval. The performance of the proposed method shows a better average IPR value of 3.51% compared to RGB and 22.92% with the HSV color model. Meanwhile, it also performs better by 36%, 5%, and 24% compared to methods CH (8,2,2), CH (8,3,3), and CH (16,4,4).

scholarly journals Silhouette index for determining optimal k-means clustering on images in different color models

2018 ◽  
Vol 7 (2.14) ◽  
pp. 105 ◽  
Author(s):  
Abd Rasid Mamat ◽  
Fatma Susilawati Mohamed ◽  
Mohamad Afendee Mohamed ◽  
Norkhairani Mohd Rawi ◽  
Mohd Isa Awang
Keyword(s):  
Image Processing ◽  
Color Model ◽  
Cluster Validation ◽  
Validation Process ◽  
Color Models ◽  
Silhouette Index ◽  
Hsv Color Model ◽  
Different Color ◽  
Lab Color Model ◽  
Cie Lab

Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.  

scholarly journals Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Edgar Chavolla ◽  
Arturo Valdivia ◽  
Primitivo Diaz ◽  
Daniel Zaldivar ◽  
Erik Cuevas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Community ◽  
Color Image ◽  
The Other ◽  
Color Image Segmentation ◽  
Color Segmentation ◽  
Color Model ◽  
Wide Range ◽  
Hsv Color Model ◽  
Relevant Topic

Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the HSV color model in order to improve the accuracy of the results obtained from the color segmentation using the K-means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the H equation in order to avoid undefined conditions and increase robustness in the color model.

A NOVEL TWO-LEVEL COLOR IMAGE RETRIEVAL METHOD

2011 ◽  
Vol 11 (03) ◽  
pp. 339-353 ◽  
Author(s):  
XING-YUAN WANG ◽  
ZHI-FENG CHEN ◽  
JIAO-JIAO YUN
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Color Image ◽  
Image Databases ◽  
Color Model ◽  
Retrieval Method ◽  
Retrieval Accuracy ◽  
Similar Images

This paper presents an effective two-level color image retrieval method based on the RGB color model. For the purpose of effectively retrieving more similar images from the digital image databases, we divide the image into different regions and set bigger weight for the region we focus on. In addition, we set different weights for each RGB component of the color image according to the main hue of it. As a result, this scheme can enhance the retrieval accuracy that is measured in terms of the recall and precision.

ROBUST IMAGE RETRIEVAL APPROACH BASED ON CLUSTERING ANALYSIS OF RE-QUANTIZED COLOR IMAGE

2007 ◽  
Vol 05 (01) ◽  
pp. 213-225 ◽  
Author(s):  
YA-LI JI ◽  
XIAO-PING CHENG ◽  
NAI-QIN FENG
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Feature Representation ◽  
Hsv Color Space ◽  
Invariant Moments ◽  
Robust Image ◽  
Color Clustering ◽  
Object Features ◽  
Large Image Databases

In this paper, we propose a robust approach about color image retrieval. It can realize fast matching in CBIR (Content-Based Image Retrieval) when we search in large image databases. Indexes root in object features of Z image which is the result of re-quantization in HSV color space, matching with a non-geometrical distance is based on objects, so time consumption pixel by pixel can be avoided. Because Z image is made up of many color clustering regions and invariant moments are used for feature representation, our approach is robust to translation, scale and rotation.

scholarly journals Innovative Sketch Board Mining for Online image Retrieval

2016 ◽  
Vol 11 (3) ◽  
pp. 13
Author(s):  
Huda Abdulaali Abdul Baqi ◽  
Ghazali Sulong ◽  
Siti Zaiton Mohd Hashim ◽  
Zinah S.Abdul jabar
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Data Communication ◽  
Recall Rate ◽  
Digital Data ◽  
Geometrical Shape ◽  
Semantic Level ◽  
Full Color ◽  
Main Challenge ◽  
High Level

Developing an accurate and efficient Sketch-Based Image Retrieval (SBIR) method in determining the resemblances between the user's query and image stream has been a never-ending quest in digital data communication era. The main challenge is to overcome the asymmetry between a binary sketch and a full-color image. We introduce a unique sketch board mining method to recover the online web images. This image conceptual retrieval is performed by matching the sketch query with the relevant terminology of selected images. A systematic sequence is followed, including the sketch drawing by the user in interpreting its geometrical shape of the conceptual form based on annotation metadata matching technique achieved automatically from Google engines, indexing and clustering the selected images via data mining. The sketch mining board being built in dynamic drawing state used a set of features to generalize sketch board conceptualization in semantic level. Images from the global repository are retrieved via a semantic match of the user's sketch query with them. Excellent retrieval of hand-drawn sketches is found to achieve the recall rate within 0.1 to 0.8 and a precision rate is 0.7 to 0.98. The proposed technique solved many problems that stat-of-art suffered from SBIR (e.g. scaling, transport, imperfect) sketch. Furthermore, it is demonstrated that the proposed technique allowed us to exploit high-level features to search the web effectively and may constitute a basis for efficient and precise image recovery tool.

Image Retrieval via CURE Clustering and SIFT Algorithms

2013 ◽  
Vol 303-306 ◽  
pp. 1573-1576 ◽  
Author(s):  
Xian Tan
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Text Clustering ◽  
Image Clustering ◽  
Original Text ◽  
Retrieval Performance ◽  
Fusion Model ◽  
Semantic Clustering ◽  
Model Image ◽  
High Level

This paper, by using the short CURE clustering algorithm and image SIFT identification method, the establishment of a kind of image semantic clustering fusion model (image text clustering fusion model, referred to as ITCFM). The model is based on component method, the original image components, original text member, image clustering member, text clustering components, clustering fusion member five parts. In ITCM model for image semantic clustering characteristics on the basis of the description and extraction. The experimental results show that ITCM model can effectively to image to describe the high-level semantic, the image retrieval effect is good, and have stable retrieval performance.

Investigation the Influence of DCT Coefficients on the Image Retrieval Performance

2013 ◽  
Vol 416-417 ◽  
pp. 1165-1169
Author(s):  
Shui Li Zhang ◽  
Jun Tang Dong ◽  
Ting Ting Shao ◽  
Xiu Ping Zheng
Keyword(s):  
Image Retrieval ◽  
Image Coding ◽  
Color Image ◽  
Image Features ◽  
Image Feature ◽  
Retrieval Performance ◽  
Satisfactory Outcome ◽  
Similar Distance ◽  
Dct Coefficients ◽  
Important Position

According to the important position of DCT in image coding, the research of image retrieval performance based on DCT is advanced. In the paper, we take color descriptors of MPEG-7 as the color image feature, and acquire new image features through giving different weights to the different elected DCT coefficients, Using Euclidean distance as the similar distance measurement to retrieve color images in DCT. Then the relationship between the choice of weight and retrieval performance is studied by experiments, and moreover the retrieval conclusion is analyzed in detail. The result shows it can acquire relatively satisfactory outcome when selecting right weights of the elected DCT coefficients based on a concrete condition.

scholarly journals Region of interest-based image retrieval techniques: a review

2020 ◽  
Vol 9 (3) ◽  
pp. 520
Author(s):  
Mardhiyah Md Jan ◽  
Nasharuddin Zainal ◽  
Shahrizan Jamaludin
Keyword(s):  
Image Retrieval ◽  
Region Of Interest ◽  
Reference Source ◽  
Global Features ◽  
Retrieval Systems ◽  
Large Databases ◽  
Indexing And Retrieval ◽  
Image Retrieval System ◽  
Specific Interests ◽  
Comprehensive Reference

<span lang="EN-US">This paper presents a review of the region of interest-based (ROI) image retrieval techniques. In this study, the techniques, the performance evaluation parameters, and databases used in image retrieval process are being reviewed. A part of an image that is considered important or a selected certain area of the image is what defines a region of interest. Retrieval performance in large databases can be improved with the application of content-based image retrieval systems which deals with the extraction of global and region features of images. The capability of reflecting users' specific interests with greater accuracy has shown to be more effective when using region-based features compared to global features. Segmentation, feature extraction, indexing, and retrieval of an image are the tasks required in retrieving images that contain similar regions as specified in a query. The idea of the region of interest-based image retrieval concepts is presented in this paper and it is expected to accommodate researchers that are working in the region-based image retrieval system field. This paper reviews the work of image retrieval researchers in the span of twenty years. The main goal of this paper is to provide a comprehensive reference source for scholars involved in image retrieval based on ROI.</span>

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