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ISO/IEC JTC 1/SC 34 N148

 

 


Date: 2000-06-10

 

 

 

 

 

 

 

Title:Korean Comments on JTC1/SC34 N0066 (A method for multimedia context based image retrieval using color moment in wavelet transformation area )             

 

 

 

 

 

Source:          Ki-Joung Kang,  Multimedia Technical Lab, Korea Telecom

 

 

Project:         

 

 

Status:            To be reviewed and discussed at Regular meeting, June 2000

 

 

 

 

 

1. Summary

 

In Granada (Spain) meeting, 19 ~ 23 April 1999, a contribution of Multimedia Retrieval System Structure and Image Retrieval (N0066)was presented. In this contribution, a method for multimedia context based image retrieval using color moment in wavelet transformation area is proposed as an explanation of the contribution, N0066.

 

2. Introduction

 

XML (Extensible Markup Language) was proposed as a standardized web document format at W3C (World Wide Web Consortium) in 1996. A standardized text format, XML, is a language for next generation internet. It resolves deficiencies of HTML (Hyper-Text Markup Language) and takes advantages of HTML and SGML (Standard Generalized Markup Language). XML makes a structured document transmitted through web and gives a method to describe user-defined document. Web applications can process XML.

W3C approved XML as a standard to make up for the weak points of HTML. XML can be considered as internet version of SGML. W3C launched SMIL (Synchronized Multimedia Integration Language) WG(Working Group). SMIL is used to integrate multimedia files stored at XML based server and to present the file at the execution point. In SC34 WG3, a standard for interactive multimedia document, ISMID, is being developed. In this contribution, a method for multimedia context based image retrieval using color moment in wavelet transformation area is proposed as an application of these standards.

 

3. Conventional Method

 

In the rapid-changing environment of communication network and multimedia technology, the study on storage, administration and retrieval for multimedia data is a primary issue.

Wavelet transformation method is used to extract characteristic vector [1]. After characteristic vector for each channel of R, G, and B is configured, the query is performed. Wang uses wavelet coefficient of level 4 in order to configure characteristic vector after wavelet transformation is performed with the help of Daubeche-8 wavelet. Direct comparison between wavelet coefficients is used to measure the similarity of images. These methods are useful in the case that overall color distribution is adopted to measure the similarity between images. But they are incongruent in the case that object shape is used for the similarity measurement. As the dimension of characteristic vector is increased, the time required to retrieve an image is extended.

 

4. Proposed Method

 

In this contribution, a context based image retrieval method using color and moment in the wavelet transformation area is proposed. In the query using the color of image, energy of wavelet coefficient in the HSV color space that is fit for the characteristic of human sense of sight. In the query using the moment, wavelet maxima of high frequency band in the wavelet transformation area is used. So query method is independent to the change of movement, rotation and size of the object. The size of characteristic vector is reduced to make the query time shorten. System configuration of the proposed method is shown in the following figure.

 

Fig 1. System Configuration

 

 

1) Extraction of characteristic vector

 

After the color space of query image is transformed to the HSV color space, 2D DWT is performed for each channel. In the configuration of characteristic vector, wavelet coefficient was not directly used in order to reduce the size of characteristic vector. The value of energy is used instead as Albuz was [3]. In the case of ordinary image, the objects in the image are usually located in the center of the image. Each band of wavelet transform area was divided in 5 fixed areas as a result of consideration of camera shutter motion. Fig 2 shows the divided areas. Energy of wavelet coefficient for each area was calculated for each HSV channel. At this time, the size of characteristic vector is 5(number of area) x 3 (number of channel) x 4byte = 60byte.

For the query using moment, 2nd central moment value for each HSV channel using wavelet maxima in the three high frequency band(LH, HL, HH) of wavelet transformation area is used as a characteristic vector.

Fig 2. Area Configuration

 

2) Measurement of similarity

 

Characteristic vector of query image and one of images stored in a database is compared to measure similarity between images[4]. Euclidean distance is used in the measurement of similarity as following formula 1:

 (formula 1)

In the user interface, there are three query methods of ?r)color, ?r)moment and ?r)color and moment[5]. In the query using color, the weight of V value is relatively low comparing to the value of hue and saturation. It reduces the effect of non-uniformity of light. In the proposed method, weight of hue and saturation is 2 and weight of value is 1. The query using moment is performed to compare wavelet maxima moment by the Euclidean distance of formula 1 in the high frequency band of each channel of H, S, and V.

 

5. Conclusion

 

In the context based image retrieval using characteristic vector, the size of characteristic vector should be reduced as far as the retrieval efficiency is not rapidly declined.

In this contribution, wavelet coefficient was not directly used as a characteristic vector in the wavelet transformation area. Energy of coefficient of each area is used to reduce the size of characteristic vector and to increase the efficiency of retrieval.

 

6. Reference

1. Charles E. Jacobs, 'Fast Multiresolution Image Query', Proceedings of the 1995 ACM SIGGRAPH, New

2. James Ze Wang, 'Content-based Image Indexing and Searching using Daubechies' Wavelet',Journal of Digital Library, 1998

3. Elif Albuz, 'Scalable Image Indexing and Retrieval using Wavelets', Technical Report, University of Delaware, 1998,11

4. Charles E. Jacobs, 'Fast Multiresolution Image Query', Proceedings of the 1995 ACM SIGGRAPH, New York, 1995

5. Y.T.Chan, 'Wavelet Basics', Kluwer Academic Plublishers,1995