In this paper, we study the peaky nature of wavelet coefficient distributions. The study shows that the wavelet coefficients cannot be effectively modeled by a single distribution. We then propose a new modeling scheme based on a Laplacian mixture model and apply it to the indexing and retrieval of image and video databases. In this work, the parameters of the model are first used to represent texture information in image retrieval. Then we explore its application to video retrieval. Traditionally, visual information is used for video indexing and retrieval. However, in some cases audio information is more helpful for finding clues to the video events. The proposed feature extraction scheme is based on the fundamental property of the wavelet transform. Therefore, it can also be adopted to analyze the audio contents of the video data. The experimental evaluation indicates the high discriminatory power of the proposed feature set. The dimension of the extracted feature vector is low, which is important for the retrieval efficiency of the system in terms of response time. User feedback is used to enhance the retrieval performance by modifying the system parameters according to the users' behavior. A nonlinear approach for defining the similarity between the two images is also explored in this work.

Audio is a rich source of information in the digital videos that can provide useful descriptors for indexing the video databases. In this paper, we model the shape of the distribution of wavelet coefficients of embedded audio with a Laplacian mixture. The distributions of wavelet coefficients are very peaky in nature. The shape of these distributions can be modeled with only two components in the Laplacian mixture with low computational complexity. The parameters of this mixture model form a low dimensional feature vector representing global similarity of the audio content of the video clips. An interactive approach involving the feature vector updating scheme is used to adapt the retrieval system to the users' needs. This relevance feedback (RF) increases the retrieval ratio substantially. A comprehensive experimental evaluation using the CNN news database has been performed.