Improving shape descriptor complexity via wavelet decomposition

Research on content based image retrieval (CBIR) has received a considerable attention as it offers solutions to overcome and complement the drawbacks of text based image retrieval (TBIR). One of the crucial studies in this system is the feature extraction process where the low level features, i.e. shape, color and texture are the common features used to describe the image content. However, studies in the past only focus on deriving good descriptors from these low level features and less attention has been given on the complexity improvement of these descriptors. This paper proposes a simple technique to reduce the complexity computations of shape feature via decomposition method. We employ the discrete wavelet transform as the decomposition technique and use the transform image content to derive the shape feature. Our method has shown an improvement of speed performance of more than 50% compared to the conventional method. The database used in this study is the MPEG7 database consisting of 1400 images with 70 classes.