A Content Based Image Retrieval Method Based on K-Means Clustering Technique

2018 ◽  
Vol 16 (1) ◽  
pp. 82-96 ◽  
Author(s):  
Mohamed Ouhda ◽  
Khalid El Asnaoui ◽  
Mohammed Ouanan ◽  
Brahim Aksasse
Keyword(s):  
Image Retrieval ◽  
Accurate Result ◽  
Efficient Algorithms ◽  
Image Databases ◽  
New Method ◽  
Content Based Image Retrieval ◽  
Computational Time ◽  
Retrieval Method ◽  
Clustering Technique ◽  
Similar Images

With the appearance of many devices that are used in image acquisition comes a large number of images every day. The rapid access to these huge collections of images and retrieval of similar images (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For this purpose, the authors apply a new method based on k-means clustering technique to match image's descriptors. This article provides a detailed view of the solution the authors have adopted and which perfectly meets their needs. For validation, they apply all of these techniques on two image databases in order to evaluate the performance of their system.

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.

scholarly journals Hybrid Method for Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining

2021 ◽  
Vol 09 (04) ◽  
Author(s):  
Dr. S. Thavamani ◽  
Keyword(s):  
Data Mining ◽  
Image Retrieval ◽  
Hybrid Method ◽  
Video Retrieval ◽  
New Method ◽  
Computational Time ◽  
Storage Space ◽  
Copy Detection ◽  
Retrieval Technique ◽  
Improved Accuracy

Duplicated images cause several problems in online sites, so these demand special attention. To address the disadvantages of frames copy detection, the Hybrid Method of Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining was proposed. We use the new method of eliminating duplicates in this example. To address the disadvantages of frames copy detection, the Hybrid Method of Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining was proposed. The new method of eliminating duplicates in this example has proposed. Using this method, you can get rid of frames that aren't relevant to the video. This makes for more precise and faster video retrieval with fewer duplicates. As a back end, this technique is implemented in C# and SQL. The findings are put to the test and compared to the current SIFT process. The results showed that the output improved accuracy while reducing storage space, computational time, and memory use.

A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

2018 ◽  
Vol 45 (1) ◽  
pp. 117-135 ◽  
Author(s):  
Amna Sarwar ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
Khurram Ashfaq Qazi ◽  
Ahmed Adnan ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Image Databases ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Bag Of Words ◽  
Visual Words ◽  
Visual Attributes ◽  
Visual Vocabulary ◽  
Novel Method

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

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