The Initial Retrieval Based on Image Segmentation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.919-921.2131 ◽

2014 ◽

Vol 919-921 ◽

pp. 2131-2134

Author(s):

Qing Hua Zhan

Keyword(s):

Image Segmentation ◽

Image Retrieval ◽

Image Matching ◽

Visual Image ◽

Segmentation Method ◽

Image Library ◽

Long Time ◽

And Performance ◽

Rich Content ◽

Initial Retrieval

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Using text-based Image Retrieval TBIR (Text-based Image Retrieval, TBIR) during retrieval will provide keywords and description of the image text matching, operation simple and quick. The defects of TBIR also, however, there are the following: (1) image library image all the need for manual annotation, time-consuming and laborious with subjective factors; (2) image semantics is rich, simple key words cannot fully express its meaning and accurate. Image Retrieval Based on regional RBIR (Region-based Image Retrieval, RBIR) first of all, by using image segmentation method, divides an image into several different regions. At last image matching is converted to match between the regions. We just need to user submits a retrieval image, greatly reducing the user's retrieval burden.

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An Image Retrieval Algorithm Based on Region Segmentation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.337 ◽

2014 ◽

Vol 596 ◽

pp. 337-341 ◽

Cited By ~ 1

Author(s):

Xiao Mei Xiong ◽

Yong Lang Liu

Keyword(s):

Image Segmentation ◽

Image Retrieval ◽

Visual Image ◽

Retrieval Algorithm ◽

Manual Annotation ◽

Region Segmentation ◽

Long Time ◽

And Performance ◽

Time Image ◽

Rich Content

Visual image, as a kind of rich content and performance of multimedia information, has been tremendously popular for a long time. Image retrieval technology is complicated than text retrieval, due to text-based image retrieval is often need manual annotation, so very laborious and individual subjective factors are there. In order to solve these problems, this paper puts forward a kind of image retrieval algorithm based on improved region segmentation. First, use of image segmentation technology, dividing the image into several regions, then to match each region and the being tested image, and obtained retrieval results in the end. It can be seen through experiment, the user only needs to submit a retrieval image, so it can greatly reduce the user's retrieval burden, and improve the efficiency of retrieval.

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Implementation and Evaluation of the Image Segmentation Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1314 ◽

2013 ◽

Vol 411-414 ◽

pp. 1314-1317

Author(s):

Li Jun Chen ◽

Yong Jie Ma

Keyword(s):

Genetic Algorithm ◽

Image Segmentation ◽

Maximum Entropy ◽

Entropy Method ◽

Segmentation Algorithm ◽

Segmentation Method ◽

Improved Genetic Algorithm ◽

And Performance ◽

Standard Genetic Algorithm ◽

Better Than

In order to achieve better image segmentation and evaluate the segmentation algorithm, a segmentation method based on 2-D maximum entropy and improved genetic algorithm is proposed in this paper, and the ultimate measurement accuracy criterion is adopted to evaluate the performance of the algorithm. The experimental results and the evaluation results show that segmentation results and performance of the proposed algorithm are both better than the segmentation method based on 2-D maximum entropy method and the standard genetic algorithm. The segmentation of the proposed algorithm is complete and spends less time; it is an effective method for image segmentation.

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HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation

Journal of Healthcare Engineering ◽

10.1155/2021/7467261 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Qixuan Sun ◽

Nianhua Fang ◽

Zhuo Liu ◽

Liang Zhao ◽

Youpeng Wen ◽

...

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Hybrid Methods ◽

Medical Image Segmentation ◽

Segmentation Method ◽

Critical Problem ◽

Rapid Convergence ◽

Benchmark Datasets ◽

And Performance ◽

Local Representations

Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generalization performance. Recently, a sequence of Transformer-based methodologies emerges in the field of image processing, which brings great generalization and performance in various tasks. On the other hand, traditional CNNs have their own advantages, such as rapid convergence and local representations. Therefore, we analyze a hybrid multimodal segmentation method based on Transformers and CNNs and propose a novel architecture, HybridCTrm network. We conduct experiments using HybridCTrm on two benchmark datasets and compare with HyperDenseNet, a network based on fully CNNs. Results show that our HybridCTrm outperforms HyperDenseNet on most of the evaluation metrics. Furthermore, we analyze the influence of the depth of Transformer on the performance. Besides, we visualize the results and carefully explore how our hybrid methods improve on segmentations.

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A level set image segmentation method based on pattern geometry classification

International Workshop on Communication Technology 2013 ◽

10.2495/cecnet130281 ◽

2014 ◽

Author(s):

Wu Hong-jiang

Keyword(s):

Image Segmentation ◽

Level Set ◽

Segmentation Method

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The 14th Annual AMSE Conference

American Journal of Islam and Society ◽

10.35632/ajis.v5i2.2727 ◽

1988 ◽

Vol 5 (2) ◽

pp. 311

Author(s):

Mozaffar Partowmah

Keyword(s):

North America ◽

Nuclear Physics ◽

Agricultural Research ◽

Social Scientists ◽

Electronic Engineering ◽

Student Paper ◽

Long Time ◽

Continuous Support ◽

And Performance ◽

Sizable Number

The 14th Annual Conferknce of the Association of Muslim Scientistsand Engineers (ASME) was held during the weekend of qufur 2628,1409/0ctober 7-9, 1988, at the Islamic Center of North America in Plainfield,Indiana. Papers presented at the Conference dealt with a variety of subjectsranging from agriculture and health sciences to car manufacturing tips,computers, industrial, civil and electronic engineering, as well as resourcemanagement and organizational behavior.Members of the AMSS (Association of Muslim Social Scientists) whoattended the AMSE Conference, participated in the sessions with undividedattention. Dr. AbdulHamid AbuSulayman, the AMSS President, in his banquetspeech, stressed the need for an active AMSE that will eventually attracta more sizable number of Muslims in North America and coordinate theirscientific efforts for their common benefit.In a session entitled “Technology Transfer,” the Japanese and Koreanapproaches were contrasted with the Muslim world approach. A highlightof the Conference was the announcement of the A1 Khwarazmi Award thatthe AMSE will award annually to a distinguished Muslim scientist or engineer.The first Al Khwarazmi Award went to Dr. M.A.K. Lodhi of Texas A&MUniversity in appreciation of his continuous support for Muslim studentsand his long-time involvement in the AMSE in addition to his scientific interestand achievements in nuclear physics and field theory.The Best Student Paper Award went to the following: 1) Abdullah M.Elramsisi of Rochester Hill, Michigan for his paper “On Model-based ImageRestoration and Performance Evaluation;” and 2) Khatib Rajab of Morgantown,West Virginia for his paper on “Agricultural Research Needs and Prioritiesin Zanzibar as perceived by Administrators and Extension Workers.”Copies of all of the presented papers were distributed at the Conferenceand will be ppblished in the conference proceedings. Preprints and reprintsmay be obtained by writing to the AMSE office at P.O. Box 38, Plainfield,Indianna, 46168 ...

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Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9918.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 9548-9551

Keyword(s):

Image Segmentation ◽

Medical Image Segmentation ◽

Experimental Result ◽

Segmentation Method ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Robust Image ◽

Multiple Clusters ◽

Single Pixel ◽

Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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