Fast Image Retrieval Using Low Frequency DCT Coefficients Tienwei Tsai Department of Computer Science and Engineering Tatung University Taipei, Taiwan, R.O.C. twt@mail.chihlee.edu.tw Yo-Ping Huang Department of Computer Science and Engineering Tatung University Taipei, Taiwan, R.O.C. yphuang@ttu.edu.tw Te-Wei Chiang Department of Information Networking Technology Chihlee Institute of Technology Taipei County, Taiwan, R.O.C. ctw@mail.chihlee.edu.tw Abstract Retrieval by image content has received great attention in the last decades. Recently, fast content- based multimedia retrieval is becoming more important as the number of compressed multimedia images and video data increases. In this paper, we investigate the use of the low frequency DCT coefficients that transformed from YUV color space as feature vectors for the retrieval of images. The proposed system allows users to select its dominant feature of query images so as to improve the retrieval performance. The experimental results show that the proposed features are sufficient for performing effective retrieval by introducing users’ opinions on the query images. Keywords: Content-based image retrieval, discrete cosine transform, color space, query by example. 1. Introduction Due to the rapid development in digital imaging, storage and networking technologies, the content- based image retrieval (CBIR) has emerged as an important area in computer vision and multimedia computing. Rather than relying on manual indexing or text descriptions by humans, CBIR systems use features that can be extracted from the image files themselves in searching a collection of images. How to achieve high correct retrieval rate at the lower feature dimensionality is an important issue in CBIR. Though a number of image features based on color, texture and shape attributes in various domains have been reported in the literature [1-4], it is still a challenge to select a good feature set for image classification. Typically, CBIR comprises both indexing and retrieval. It has been observed that the problem of image indexing has been approached by two different groups, using distinctive methodologies [5]. One group, clustered largely around the more traditional, text-oriented library informatics world, has approached the problem as a task in efficiently adding text descriptors to images. The second group, clustered largely around computer science work, has approached the problem through image processing. This second group has come to be largely identified with CBIR. We also note a third research direction, proposed by Goodrum [6], that seeks to combine image processing with text labelling of images. Several techniques have been proposed to the problem of finding or indexing images based on their contents. For example, methods such as Fourier Transform, Discrete Cosine Transform (DCT), Hough Transform, Wavelet Transform, Gabor Transform, and Hadamard transform coefficients have been used as engine in CBIR system. Each method used has strong and weak points. In our approach, the DCT is used to extract low-level texture features for fast retrieval of images. Due to the energy compacting property of DCT, much of the signal energy has a tendency to lie at low frequencies. In other words, most of the signal energy is preserved in a relatively small amount of DCT coefficients. For instance, in the case of OCR, 84% signal energy appears in the 2.8% DCT coefficients, which corresponds to a saving of 97.2% [9]. This means that the coefficients of the high- frequency components are close to zero, and therefore negligible in most cases. Moreover, most of the current images are stored in JPEG format and the image-compression technology at the heart of the JPEG standard is DCT. As to other transform-based feature extraction methods like wavelet transform, the image decompression of inverse DCT is needed for the DCT-coded images. We hope that the feature sets derived here can be generated directly in DCT domain so as to reduce the processing time. Generally, the CBIR systems follow the paradigm of representing images via a set of feature attributes, such as color, texture, shape and layout. Selecting too many features can probably inhibit important features to be taken into account. In this paper, we intend to achieve an acceptable retrieval result using a content descriptor formed by only one single feature. For large image database, therefore, the system can be built to support efficient and effective accesses to image data with a very low computational load. Our system mainly focuses on the texture feature in YUV color space. The effective image feature vector can be derived from the low frequency DCT coefficients which are transformed from the texture feature of an image. A retrieval task is performed by matching the feature vector of the query image with