An Image Retrieval Approach Based on Dominant Wavelet Features Presented by Te-Wei Chiang 2006/4/1 Outline 1. Introduction 2. Feature Extraction 3. Similarity Measurement 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 wavelet transform is used to extract low-level texture features. Related Works Content-based image retrieval is a technology to search for similar images to a query based only on the image pixel representation. However, the query based on pixel information is quite time-consuming Some of the systems employ color histograms. The histogram measures are only dependent on summations of identical pixel values and do not incorporate orientation and position. 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. 2. 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) = R(x, y) G(x, y) B(x, y), U(x, y) = (B(x, y) - Y(x, y)), and V(x, y) = (R(x, y) - Y(x, y)). Wavelet Transform Mallat' s pyramid algorithm Fig. 1 Fig. 2 Distance 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 LL(k) subband can be defined as Therefore, the distance between Q and Xn can be modified as the weighted combination of LL (k), LH (k), HL (k), HH (k) : 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. Fig. 3 The GUI of our image retrieval system and the retrieved results based on the approximations at wavelet level 1. 5. Conclusions In this paper, we propose a CBIR method based on the DWT. Each image is first transformed from YUV color space; then the Y component of the image is further transformed to extract four types of features at each resolution level: approximations, horizontal details, vertical details and diagonal details. Our CBIR system also provides a GUI such that users can adjust the weight of each wavelet feature according to their expectations. Then, users can retrieve the desired images from the image database via the query image and the systems interactive GUI. Future Works Since only preliminary experiment has been made to test our approach, a lot of works should be done to improve this system. For each type of feature we will continue investigating and improving its ability of describing the image and its performance of similarity measuring. Since several features may be used simultaneously, it is necessary to develop a scheme that can integrate the similarity scores resulting from the matching processes. Thank You !!!