ContentBased Image Retrieval Using Block Discrete Cosine Transform

Content-Based Image Retrieval Using Block Discrete Cosine Transform Presented by Te-Wei Chiang Department of Information Networking Technology Chihlee Institute of Technology 2005/12/19

Outline 1. Introduction 2. Problem Formulation 3. Proposed Image Retrieval System 4. Experimental Results 5. 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 DCT is used to extract lowlevel texture features. – the energy compacting property of DCT

• In this paper, we propose a content-based image retrieval method based on block DCT. • In the image database establishing phase, each image is first transformed from the standard RGB color space to the YUV space; then Y component of the image is further transformed to the DCT domain. • In the image retrieving phase, the system compares the most significant DCT coefficients of the Y component of the query image and those of the images in the database and find out good matches. • Moreover, based on the observation that block DCT can reduce the computational complexity of each DCT operation but may lose global view of the image being processed, in this paper, we will investigate the relation between the precision rate in image retrieval and the block size of the block DCT.

2. Problem Formulation • Let I be the image database with I : = {Xn | n = 1, . . . , N} where Xn is an image represented by a set of features: Xn : = {xn m | m = 1, . . . , M}. – N and M are the number of images in the image database and the number of features, respectively. • To query the database, the dissimilarity (or distance) measure D(Q, Xn) is calculated for each n as – dm is the distance function or dissimilarity measure for the mth feature and wm R is the weight of the mth feature. – Query image Q : = {qm | m = 1, …, M}. – For each n, holds. By adjusting the weights wm it is possible to emphasize properties of different features.

3. The Proposed Image Retrieval System Figure 1. The proposed system architecture.

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 Transformation RGB (Red, Green, and Blue) -> YUV (Luminance and Chroma channels)

YUV color space • YUV is based on the CIE Y primary, and also 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 Cosine Transform 2 Feature Extraction via DCT • The DCT coefficients F(u, v) of an N×N image represented by f(i, j) can be defined as where

Characteristics of DCT • the DC coefficient (i. e. F(0, 0)) represents the average energy of the image; • all the remaining coefficients contain frequency information which produces a different pattern of image variation; and • the coefficients of some regions represent some directional information.

Similarity Measurement • Distance measure – the sum of absolute differences (SAD): avoid multiplications. – the sum of squared differences (SSD): exploit the energy preservation property of DCT • The distance between Q and Xn under the low frequency block of size k×k can be defined as • For block DCT, where an image is divided into nonoverlapping S×S blocks, the distance between Q and Xn can be defined as the summation of the distances under each block.

4. Experimental Results • 1000 images downloaded from the WBIIS database are used to demonstrate the effectiveness of our system. • The user can query by an external image or an image from the database. • To evaluate the retrieval efficiency of the proposed method, we use the performance measure, the precision rate, as follows: where Rr is the number of relevant retrieved items, and Tr is the number of all retrieved items.

Figure 1. Retrieved results using ordinary DCT with low frequency DCT coefficients of size 2× 2.

Figure 2. Retrieved results using ordinary DCT with low frequency DCT coefficients of size 4× 4.

Figure 3. Precision rate of the test image over k using ordinary DCT.

Figure 4. Precision rate of the test image over k using 2 x 2 block DCT.

Figure 5. Precision rate of the test image over k using 3 x 3 block DCT.

Figure 6. Precision rate of the test image over k using 4 x 4 block DCT.

Figure 7. Precision rate of the test image over k using 5 x 5 block DCT.

Figure 8. Precision rate of the test image over k using 6 x 6 block DCT.

Figure 9. Precision rate of the test image over k using 7 x 7 block DCT.

Figure 10. Precision rate of the test image over k using 8 x 8 block DCT.

Figure 11. Precision rate of the test image over k using 16 x 16 block DCT.

Figure 12. Retrieved results using 2× 2 block DCT with low frequency DCT coefficients of size 2× 2.

Figure 13. The best precision rate that can be obtained from the block DCT with differing S value.

5. Conclusions • In this paper, a content-based image retrieval method based on block DCT is proposed. • To achieve Qb. E, the system compares the most significant DCT coefficients of the query image and those of the images in the database and find out good matches. • Based on the observation that block DCT can reduce the computational complexity of each DCT operation but may lose global view of the image being processed, we are motivated to investigate the relation between the precision rate in image retrieval and the block size of the block DCT. • Experiments show that the block DCT with larger block size would perform better than the block DCT with small block size and the ordinary DCT in terms of the precision rate of the content-based image retrieval.

Thank You !!!