Giang P. Nguyen

Interactive access to large image collections using similarity-based visualization

Image collections are getting larger and larger. To access those collections, systems for managing, searching, and browsing are necessary. Visualization plays an essential role in such systems. Existing visualization systems do not analyze all the problems occurring when dealing with large visual collections. In this paper, we make these problems explicit. From there, we establish three general requirements: overview, visibility, and structure preservation. Solutions for each requirement are proposed, as well as functions balancing the different requirements. We present an optimal visualization scheme, supporting users in interacting with large image collections. Experimental results with a collection of 10,000 Corel images, using simulated user actions, show that the proposed scheme significantly improves performance for a given task compared to the 2D grid-based visualizations commonly used in content-based image retrieval.

MediaMill: exploring news video archives based on learned semantics

In this technical demonstration we showcase the MediaMill system. A search engine that facilitates access to news video archives at a semantic level. The core of the system is an unprecedented lexicon of 100 automatically detected semantic concepts. Based on this lexicon we demonstrate how users can obtain highly relevant retrieval results using query-by-concept. In addition, we show how the lexicon of concepts can be exploited for novel applications using advanced semantic visualizations. Several aspects of the MediaMill system are evaluated as part of our TRECVID 2005 efforts.

Optimization of interactive visual-similarity-based search

At one end of the spectrum, research in interactive content-based retrieval concentrates on machine learning methods for effective use of relevance feedback. On the other end, the information visualization community focuses on effective methods for conveying information to the user. What is lacking is research considering the information visualization and interactive retrieval as truly integrated parts of one content-based search system. In such an integrated system, there are many degrees of freedom like the similarity function, the number of images to display, the image size, different visualization modes, and possible feedback modes. To base the optimal values for all of those on user studies is unfeasible. We therefore develop search scenarios in which tasks and user actions are simulated. From there, the proposed scheme is optimized based on objective constraints and evaluation criteria. In such a manner, the degrees of freedom are reduced and the remaining degrees can be evaluated in user studies. In this article, we present a system that integrates advanced similarity based visualization with active learning. We have performed extensive experimentation on interactive category search with different image collections. The results using the proposed simulation scheme show that indeed the use of advanced visualization and active learning pays off in all of these datasets.

Interactive Search by Direct Manipulation of Dissimilarity Space

In this paper, we argue to learn dissimilarity for interactive search in content based image retrieval. In literature, dissimilarity is often learned via the feature space by feature selection, feature weighting or by adjusting the parameters of a function of the features. Other than existing techniques, we use feedback to adjust the dissimilarity space independent of feature space. This has the great advantage that it manipulates dissimilarity directly. To create a dissimilarity space, we use the method proposed by Pekalska and Duin, selecting a set of images called prototypes and computing distances to those prototypes for all images in the collection. After the user gives feedback, we apply active learning with a one-class support vector machine to decide the movement of images such that relevant images stay close together while irrelevant ones are pushed away (the work of Guo ). The dissimilarity space is then adjusted accordingly. Results on a Corel dataset of 10000 images and a TrecVid collection of 43907 keyframes show that our proposed approach is not only intuitive, it also significantly improves the retrieval performance.

Similarity learning via dissimilarity space in CBIR

In this paper, we introduce a new approach to learn dissimilarity for interactive search in content based image retrieval. In literature, dissimilarity is often learned via the feature space by feature selection,feature weighting or a parameterized function of the features. Different from existing techniques, we use relevance feedback to adjust dissimilarity in a dissimilarity space. To create a dissimilarity space, we use Pekalskas method [15]. After the user gives feed-back, we apply active learning with one-class SVM on this space. Results on a Corel dataset of 10000 images and a TrecVid collection of 43907 keyframes show that our proposed approach can improve the retrieval performance over the feature space based approach.

Mathematical Model of Nerve Fiber Activation During Low Back Peripheral Nerve Field Stimulation: Analysis of Electrode Implant Depth

The lower back is the most common location of pain experienced by one‐fifth of the European population reporting chronic pain. A peripheral nerve field stimulation system, which involves electrodes implanted subcutaneously in the painful area, has been shown to be efficacious for low back pain. Moreover, the predominant analgesic mechanism of action is thought to be via activation of peripheral Aβ fibers. Unfortunately, electrical stimulation also might coactivate Aδ fibers, causing pain or unpleasantness itself. The aim of this study was to investigate at which implant depth Aβ‐fiber stimulation is maximized, and Aδ‐fiber minimized, which in turn should lead to therapy optimization.

User Strategies in Video Retrieval: A Case Study

In this paper we present the results of a user study that was conducted in combination with a submission to TRECVID 2003. Search behavior of students querying an interactive video-retrieval system was analyzed. 242 Searches by 39 students on 24 topics were assessed.Questionnaire data, logged user actions on the system, and a quality measure of each search provided by TRECVID were studied. Analysis of the results at various stages in the retrieval process suggests that retrieval based on transcriptions of the speech in video data adds more to the average precision of the result than content-based retrieval. The latter is particularly useful in providing the user with an overview of the dataset and thus an indication of the success of a search.

An user based framework for salient detail extraction

We consider the interaction with salient details in the image i.e. points, lines, and regions. Interactive salient detail definition goes further than summarizing the image into a set of salient details since the saliency of details depends on the context, the application and the user. We propose an interaction framework for salient details from the perspective of the user, which dynamically updates the user- and context-dependent definition of saliency based on relevance feedback. A number of instantiations of the framework are presented.

Optimizing similarity based visualization in content based image retrieval

In any CBIR system, visualization is important, either to show the final result to the user or to form the basis for interaction. Advanced systems use 2D similarity based visualization which shows not only the information of one image itself but also the relations between images. A problem in interactive 2D visualization is the overlap between the images displayed. This obviously reduces the search capability. Simply spreading the images on the screen space will not preserve the relations between them. In this paper, we propose a visualization scheme which reduces the overlap as well as preserves the general distribution of the images displayed. Results show that an effective balance between display of structures and limited overlap can be achieved

Probabilistic model for individual assessment of central hyperexcitability using the nociceptive withdrawal reflex: a biomarker for chronic low back and neck pain.

BackgroundThe nociceptive withdrawal reflex (NWR) has been proven to be a valuable tool in the objective assessment of central hyperexcitability in the nociceptive system at spinal level that is present in some chronic pain disorders, particularly chronic low back and neck pain. However, most of the studies on objective assessment of central hyperexcitability focus on population differences between patients and healthy individuals and do not provide tools for individual assessment. In this study, a prediction model was developed to objectively assess central hyperexcitability in individuals. The method is based on statistical properties of the EMG signals associated with the nociceptive withdrawal reflex. The model also supports individualized assessment of patients, including an estimation of the confidence of the predicted result.Resultsup to 80% classification rates were achieved when differentiating between healthy volunteers and chronic low back and neck pain patients. EMG signals recorded after stimulation of the anterolateral and heel regions and of the sole of the foot presented the best prediction rates.ConclusionsA prediction model was proposed and successfully tested as a new approach for objective assessment of central hyperexcitability in the nociceptive system, based on statistical properties of EMG signals recorded after eliciting the NWR. Therefore, the present statistical prediction model constitutes a first step towards potential applications in clinical practice.