An Iconic and Semantic Content Based Retrieval System for Histological Images

2000 ◽  
pp. 384-395 ◽  
Author(s):  
Ringo W. K. Lam ◽  
Kent K. T. Cheung ◽  
Horace H. S. Ip ◽  
Lilian H. Y. Tang ◽  
R. Hanka
Keyword(s):  
Retrieval System ◽  
Semantic Content ◽  
Content Based Retrieval ◽  
Histological Images

Content-Based Retrieval Concept

2005 ◽  
pp. 564-568
Author(s):  
Yung-Kuan Chan ◽  
Chin-Chen Chang
Keyword(s):  
Image Retrieval ◽  
Search Engines ◽  
Retrieval System ◽  
Human Beings ◽  
Content Based Retrieval ◽  
The Past ◽  
Efficient Management ◽  
Image Retrieval System

Because of the demand for efficient management in images, much attention has been paid to image retrieval over the past few years. The text-based image retrieval system is commonly used in traditional search engines (Ratha et al., 1996), where a query is represented by keywords that are usually identified and classified by human beings. Since people have different understandings on a particular image, the consistency is difficult to maintain. When the database is larger, it is arduous to describe and classify the images because most images are complicated and have many different objects. There has been a trend towards developing the content-based retrieval system, which tries to retrieve images directly and automatically based on their visual contents.

ASSERT: A Physician-in-the-Loop Content-Based Retrieval System for HRCT Image Databases

1999 ◽  
Vol 75 (1-2) ◽  
pp. 111-132 ◽  
Author(s):  
Chi-Ren Shyu ◽  
Carla E. Brodley ◽  
Avinash C. Kak ◽  
Akio Kosaka ◽  
Alex M. Aisen ◽  
...  
Keyword(s):  
Retrieval System ◽  
Image Databases ◽  
Content Based Retrieval

Content-based retrieval system for image using human face information

1999 ◽  
Author(s):  
HwangSeok Oh ◽  
JongSeung Park ◽  
DukHo Chang ◽  
GilRok Oh
Keyword(s):  
Retrieval System ◽  
Human Face ◽  
Content Based Retrieval

scholarly journals A content-based retrieval system for UAV-like video and associated metadata

2008 ◽  
Author(s):  
N. E. O'Connor ◽  
T. Duffy ◽  
P. Ferguson ◽  
C. Gurrin ◽  
H. Lee ◽  
...  
Keyword(s):  
Retrieval System ◽  
Content Based Retrieval

scholarly journals Content Based Image Retrieval (CBIR) by Statistical Methods

2020 ◽  
Vol 17 (2(SI)) ◽  
pp. 0694
Author(s):  
Fathala Ali et al.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Semantic Content ◽  
T Test ◽  
Statistical Characteristics ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Image Retrieval System ◽  
Level Of Significance ◽  
Second Procedure

            An image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T- test to measure the independence between more than images, (coefficient of correlate, T- test, Level of significance, find the decision), and, through experimental test, it was found that this proposed method of retrieval technique is powerful than the classical retrieval System.

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