Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques, designed around query refinement based on relevance feedback, suffer from slow convergence, and do not guarantee to find intended targets. To address these limitations, we propose several efficient query point movement methods. We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency. We also consider strategies to minimize the effects of users' inaccurate relevance feedback. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of required iterations and improves overall retrieval performance. The experimental results also confirm that our approach can always retrieve intended targets even with poor selection of initial query points.

Image clustering is to derive a salient grouping of images such that similar ones are placed in the same cluster, which is useful in many applications. In this paper, we propose a constrained clustering algorithm, which leverages the collected user query log to guide the clustering process. Our method models a set of images as a graph and randomly contracts two vertices into a meta vertex iteratively with regarding to their similarity until the desired number of image groups has been reached. The experimental results demonstrate the superiority of our proposal.

Nowadays’ image retrieval techniques focus on modifying a query contour based on the relative correlations of relevant images. The spatial relationship between the relevant images and all the other images in database (i.e. the directional information) is not explored and utilized well. From the aspect of feature space, when a user selects relevant images, the direction to the potential relevant images is implicitly expressed. In this paper, we propose a Dynamic Directional Navigation (DDN) system to explore the directional information and navigate in the data space to find similar images. Multiple queries will be used when the relevant images point to different directions. The experiment results indicate that our technique can address the semantic gap well in Content-Based Image Retrieval (CBIR) and achieves better precision and recall than the previous techniques.

High-resolution 3D medical image data are typically huge in volume, varying from several hundred megabytes to several dozen gigabytes. Since such large-size data sets severely stress storage systems and networks, most existing Web-based interactive 3D medical imaging applications, such as 3D slicer and region-of-interest viewing, deal with only low-or medium-resolution images. In this paper, we introduce a single instance storage design, a client sharing technique, and a corresponding query processing method to enhance our existing remote medical image viewing system. We give performance results to show that the new design can significantly reduce storage and communication bandwidth requirements. Furthermore, it is more scalable to support a larger number of concurrent users.

With the proliferation of handheld devices, the demand of multimedia information retrieval on mobile devices has attracted more attention. A relevance feedback information retrieval process usually includes several rounds of query refinement. Each round incurs exchange of tens of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay making the system unfriendly to use. We address this issue by considering a client-side relevance feedback (CRF) technique. Since relevance feedback is done on client side, system response is instantaneous significantly enhancing system usability. Furthermore, since the server is not involved in relevance feedback processing, it is able to support thousands more users simultaneously. Our experimental results indicate that this scheme also effectively address the semantic gap in content-based image retrieval.

Today’s Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (k- NN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm — the Query Decomposition model - that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results to illustrate the effectiveness and efficiency of this new approach to content-based image retrieval.

In this paper, we argue that two inferences can be made from the results of recent studies: the continuity of image representation and the non-homogeneity of feature space. These characteristics enable the identification of points that satisfy the constraints established in multipoint queries in any orthogonal feature representations. The set can be described by a linear system of equations with constraints. We look at several existing techniques and propose one that can retrieve the set efficiently. We evaluated the performance of our present technique on large sets of images. The results indicate that the new representation matches semantic classes of images very well. The superiority of our approach is evident in the experimental results and in actual implementation.