98,874 research outputs found

    Recent Developments in Cultural Heritage Image Databases: Directions for User-Centered Design

    Get PDF
    published or submitted for publicatio

    Evaluating Relevance Feedback: An Image Retrieval Interface for Children

    Get PDF
    Studies on information retrieval for children are not yet\ud common. As young children possess a limited vocabulary\ud and limited intellectual power, they may experience more\ud difficulty in fulfilling their information need than adults.\ud This paper presents an image retrieval user interface that\ud is specifically designed for children. The interface uses relevance feedback and has been evaluated by letting children\ud perform different search tasks. The tasks were performed\ud using two interfaces; a more traditional interface - acting as a control interface - and the relevance feedback interface. \ud One of the remarkable results of this study is that children\ud did not favor relevance feedback controls over traditional\ud navigational controls

    An Integrated Content and Metadata based Retrieval System for Art

    No full text
    In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type

    The Eurovision St Andrews collection of photographs

    Get PDF
    This report describes the Eurovision image collection compiled for the ImageCLEF (Cross Language Evaluation Forum) evaluation exercise. The image collection consists of around 30,000 photographs from the collection provided by the University of St Andrews Library. The construction and composition of this unique image collection are described, together with the necessary information to obtain and use the image collection

    Video information retrieval using objects and ostensive relevance feedback

    Get PDF
    In this paper, we present a brief overview of current approaches to video information retrieval (IR) and we highlight its limitations and drawbacks in terms of satisfying user needs. We then describe a method of incorporating object-based relevance feedback into video IR which we believe opens up new possibilities for helping users find information in video archives. Following this we describe our own work on shot retrieval from video archives which uses object detection, object-based relevance feedback and a variation of relevance feedback called ostensive RF which is particularly appropriate for this type of retrieval

    Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval

    Full text link
    This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a hierarchical chain of abstraction from pixel inputs to concise and descriptive representations. The current work explores this capacity in the realm of document analysis, and confirms that this representation strategy is superior to a variety of popular hand-crafted alternatives. Experiments also show that (i) features extracted from CNNs are robust to compression, (ii) CNNs trained on non-document images transfer well to document analysis tasks, and (iii) enforcing region-specific feature-learning is unnecessary given sufficient training data. This work also makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis
    corecore