Image Retrieval Based on the Wavelet Features of Interest

Te-Wei Chiang, Tienwei Tsai, and Yo-Ping Huang

2006/10/10

Outline

1. Introduction 1. Introduction

2. Proposed Image Retrieval System2. Proposed Image Retrieval System

3. Experimental Results3. Experimental Results

4. Conclusions4. Conclusions

1. Introduction

• Two approaches for image retrieval: – query-by-text (QBT): annotation-based image

retrieval (ABIR)– query-by-example (QBE): content-based

image retrieval (CBIR)

• Standard CBIR techniques can find the images exactly matching the user query only.

• In QBE, the retrieval of images basically has been done via the similarity between the query image and all candidates on the image database. – Euclidean distance

• Transform type feature extraction techniques– Wavelet, Walsh, Fourier, 2-D moment, DCT, and

Karhunen-Loeve.

• In our approach, the wavelet transform is used to extract low-level texture features.

• In this paper, we focus on the QbE approach. The user gives an example image similar to the one he/she is looking for.

• Finally, the images in the database with the smallest distance to the query image will be given, ranking according to their similarity.

System Architecture

1. This system consists of two major modules: • the feature extraction module• the similarity measuring module.

2. In the image database establishing phase:• each image is first transformed from the standard RGB color

space to the YUV space; • then each component (i.e., Y, U, and V) of the image is further

transformed to the wavelet domain.

3. In the image retrieving phase:• the similarity measuring module compares the most significant

wavelet coefficients of the Y, U, and V components of the query image and those of the images in the database and find out good matches.

4. To benefit from the user-machine interaction, a GUI is developed, allowing users to adjust weights for each feature according to their preferences.

Feature Extraction

• Features are functions of the measurements performed on a class of objects (or patterns) that enable that class to be distinguished from other classes in the same general category.

• Color Space TransformationRGB (Red, Green, and Blue) ->

YUV (Luminance and Chroma channels)

YUV color space

• YUV is based on the Y primary and chrominance.

– The Y primary was specifically designed to follow the luminous efficiency function of human eyes.

– Chrominance is the difference between a color and a reference white at the same luminance.

• The following equations are used to convert from RGB to YUV spaces:

– Y(x, y) = 0.299 R(x, y) + 0.587 G(x, y) + 0.114 B(x, y),

– U(x, y) = 0.492 (B(x, y) - Y(x, y)), and

– V(x, y) = 0.877 (R(x, y) - Y(x, y)).

Discrete Wavelet Transform

• Mallat' s pyramid algorithm

Similarity Measurement

• In our experimental system, we define a measure called the sum of squared differences (SSD) to indicate the degree of distance (or dissimilarity).

• The distance between Q and Xn under the Y component and LL(k) subband can be defined as

m n

kx

kqnYLL

nmYLLnmYLLXQDn

k

2)()( ),(),(),()(

• The distance between Q and Xn under the component Y can be defined as the weighted combination of LL(k) , LH(k) , HL(k) , HH(k) :

),(),((),( )()()()(

1nYLHYLH

K

knYLLYLLnY XQDwXQDwXQD kkkk

)),(),( )()()()( nYHHYHHnYHLYHLXQDwXQDw kkkk

• Likewise, the distances between Q and Xn under the component U and V can be defined.

• Then, the overall distance between Q and Xn can be defined as :

),(),(),(),( nVVnUUnYYn XQDwXQDwXQDwXQD

5. Experimental Results

• 1000 images downloaded from the WBIIS database are used to demonstrate the effectiveness of our system.

• The images are mostly photographic and have various contents, such as natural scenes, animals, insects, building, people, and so on.

6. Conclusions

• In this paper, a content-based image retrieval method that based on DWT is proposed.

• To achieve QBE, the system compares the most significant wavelet coefficients of the Y, U, and V components of the query image and those of the images in the database and find out good matches by the help of users’ cognition ability.

• Since there is no feature capable of covering all aspects of an image, the discrimination performance is highly dependent on the selection of features and the images involved.

• Since several features are used simultaneously, it is necessary to integrate similarity scores resulting from the matching processes.

Future Works

• For each type of feature we will continue investigating and improving its ability of describing the image and its performance of similarity measuring.

Thank You !!!