Image Retrieval System based on Color Global and Local Features Combined with GLCM for Texture Features

Alnihoud, Jehad Q. Odeh

doi:10.14569/ijacsa.2018.090923

Cited by 3 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Average Precision Average Time (Sec.) in [1] 61.42% 1.53 in [4] 72.56% 0.67 Proposed 84.57% 0.51 [9] 83.15% in [20] 81.23% in [12] 82.47% in [22] 83.30% in [15] 83.08% in [23] 58.52% in [16] 83.22% in [24] 78.52% in [19] 83.12% in [25] 78.92% in [26] 76.57% in [28] 83.29% in [29] 82.79% Proposed 84.57%…”

Section: Methodsmentioning

confidence: 99%

“…The proposed hierarchical approach was applied to the standard INRIA dataset. A two phases approach was proposed in [16] to retrieve images from the data set based on color and texture. In the first phase, the HSV global color histogram was used and an automatic cropping technique was introduced to accelerate the features extraction process and enhanced the retrieval accuracy.…”

Section: -Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

RajaeeNejad,

Mohanna

2023

jist

View full text Add to dashboard Cite

One of the most important databases in the e-commerce is tile and ceramic database, for which no specific retrieval method has been provided so far. In this paper, a method is proposed for the content-based retrieval of digital images of tiles and ceramics databases. First, a database is created by photographing different tiles and ceramics on the market from different angles and directions, including 520 images. Then a query image and the database images are divided into nine equal sub-images and all are grouped based on their sub-images. Next, the selected color and texture features are extracted from the sub-images of the database images and query image, so, each image has a feature vector. The selected features are the minimum features that are required to reduce the amount of computations and information stored, as well as speed up the retrieval. Average precision is calculated for the similarity measure. Finally, comparing the query feature vector with the feature vectors of all database images leads to retrieval. According to the retrieving results by the proposed method, its accuracy and speed are improved by 16.55% and 23.88%, respectively, compared to the most similar methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: -Related Workmentioning

confidence: 99%

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

RajaeeNejad,

Mohanna

2023

jist

View full text Add to dashboard Cite

show abstract

“…Global and local colour features are widely used in CBIR. Many research attempts is made to localize colour features by dividing images into equal sub images or dividing images to overlap sub images [9]. Colour-based image retrieval utilising local features overcomes the limitations of global features like; the depiction of spatial distribution of colours.…”

Section: A Colour Features Extractionmentioning

confidence: 99%

“…The following figures (4)(5)(6)(7)(8)(9)(10)(11) show an example to explain the different cases of JDE filling. Consider figure 4 as the original image after edge detection is applied.…”

Section: Th Casementioning

confidence: 99%

Shape based Image Retrieval Utilising Colour Moments and Enhanced Boundary Object Detection Technique

Alnihoud¹

2018

ijacsa

View full text Add to dashboard Cite

The need for automatic object recognition and retrieval have increased rapidly in the last decade. In contentbased image retrieval (CBIR) visual cues such as colour, texture, and shape are the most prominent features used. Texture features are not considered as a significant discriminator unless it is integrated with colour features. Colour-based image retrieval uses global and/or local features has proved its ability to retrieve images with a high degree of accuracy. In contrast, shape-based retrieval is still suffering from numerous unsolved problems such as precise edge detection, overlapping objects, and high cost of feature extraction. In this paper, global colour features are utilized to discriminate unrelated images. Furthermore, a novel hybrid approach is proposed, consisting of a combination of boundary-based shape descriptor (BBSD) and region-based shape descriptor (RBSD), image retrieval. An enhanced object boundary detection (EBOD) is proposed, which uses canny edge detector to detect shape boundaries, with morphological opening to remove isolated nodes. Subsequently, morphological closing is utilized to solidify objects within the target image to enhance shape-based features representation. Finally, shape features are extracted and Euclidean distance measure with different threshold values to measure the similarity between feature vectors is adopted. Five semantic categories of WANG image database are selected to test the proposed approach. The results of experiments are promising, when compared with most common related approaches.

show abstract

Visual Analysis on the Research of Color-based Image Retrieval

Su,

2023

FHSS

View full text Add to dashboard Cite

This study retrieved relevant literature on color-based image retrieval in the Web of Science core database, and used bibliometrics and CiteSpace 6.1.R2 software to visually analyze the authors, institutions, and keywords of literature published in this field from 2005 to 2023. This study summarized the current status and development trend of color-based image retrieval and combined information extraction to select and include 716 articles for analysis. The results showed that: (1) In the last 20 years, the number of publications in this field has gone through a slow budding phase, a fluctuating growth phase, and an explosive growth phase; research in this area has been highly active in recent three years. (2) The authors of this field have a relatively even distribution of publications and some authors have formed close collaborative groups. (3) The research institutions mainly include research institutes and comprehensive universities, which have close collaborations with each other. (4) Co-occurrence and clustering analysis of keywords showed that research in this field is mainly focused on the main paths, research methods, and technological tools of color-based image retrieval, as well as key objects and potential influencing factors in the technological process. (5) Emerging keywords analysis predicted that more precise color description and extraction technology will be the focus of future research. In conclusion, color-based image retrieval is still in a hot research and technology explosion phase, and cooperation between countries, institutions, and authors should be strengthened to conduct more in-depth research.

show abstract

Image Retrieval System based on Color Global and Local Features Combined with GLCM for Texture Features

Cited by 3 publications

References 15 publications

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

Shape based Image Retrieval Utilising Colour Moments and Enhanced Boundary Object Detection Technique

Visual Analysis on the Research of Color-based Image Retrieval

Contact Info

Product

Resources

About