1999
DOI: 10.1016/s0167-8655(98)00100-7
|View full text |Cite
|
Sign up to set email alerts
|

Color and spatial feature for content-based image retrieval

Abstract: Most of the currently available image database systems provide a text-based retrieval function called keyword retrieval, where users specify`keywords' such as titles, attributes, and categories of themes. But many times it is not easy for users to specify suitable keywords for a particular retrieval. Besides, building a large image database with complete description of contents is a very dicult task. In this paper, we present a content-based retrieval method which obviates the need to describe certain contents… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
24
0

Year Published

2000
2000
2014
2014

Publication Types

Select...
6
3

Relationship

0
9

Authors

Journals

citations
Cited by 58 publications
(26 citation statements)
references
References 15 publications
0
24
0
Order By: Relevance
“…the square root of the second order instant of wavelet coefficients. The LUV color space [14,15] mentioned in this paper, to extract the color components where L encrypts luminance; U and V encrypt color information (chrominance). The other three features acquired by applying Daubechies-4 wavelet alter to the L component of the image.…”
Section: Feature Extraction and Representationmentioning
confidence: 99%
“…the square root of the second order instant of wavelet coefficients. The LUV color space [14,15] mentioned in this paper, to extract the color components where L encrypts luminance; U and V encrypt color information (chrominance). The other three features acquired by applying Daubechies-4 wavelet alter to the L component of the image.…”
Section: Feature Extraction and Representationmentioning
confidence: 99%
“…Kankanhalli et al [81] presented a context based image retrieval m ethod based on colour and spatial feature distributions. A colour and spatial clustering al gorithm was adopted and the colour and spatial feature similarity of the two images were compared using a similarity measure.…”
Section: S U M M Arymentioning
confidence: 99%
“…Second, users are sometimes not satisfied because keyword annotation is very subjective. Against such a backdrop, various studies on content-based image retrieval have been conducted with great success [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In CBIR, image features such as color, shape and texture are used, queries such as example images and sketch images are used.…”
Section: Introductionmentioning
confidence: 99%