Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) form large, unst...
详细信息
ISBN:
(纸本)9781450320016
Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) form large, unstructured repositories. The goal of VIR is to retrieve the highest number of relevant matches to a given query (often expressed as an example image and/or a series of keywords). In its early years (1995-2000) the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the semantic gap (the lack of coincidence between an image's visual contents and its semantic interpretation) required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on. In this tutorial, we present an overview of visual information retrieval (VIR) concepts, techniques, algorithms, and applications. Several topics are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for content-based image retrieval (CBIR) written by Mathias Lux. After motivating the topic, we briefly review the fundamentals of information retrieval, present the most relevant and effective visual descriptors currently used in VIR, the most common indexing approaches for visual descriptors, the most prominent machine learning techniques used in connection with contemporary VIR solutions, as well as the challenges associa
To keep up with the frantic pace at which devices come out, drivers need to be quickly developed, debugged and tested. Although a driver is a critical system component, the driver development process has made little (...
To keep up with the frantic pace at which devices come out, drivers need to be quickly developed, debugged and tested. Although a driver is a critical system component, the driver development process has made little (if any) progress. The situation is particularly disastrous when considering the hardware operating code (i.e., the layer interacting with the device). Writing this code often relies on inaccurate or incomplete device documentation and involves assembly-level operations. As a result, hard-ware operating code is tedious to write, prone to errors, and hard to debug and *** paper presents a new approach to developing hardware operating code based on an Interface Definition language (IDL) for hard-ware functionalities, named Devil. This IDL allows a high-level definition of the communication with a device. A compiler automatically checks the consistency of a Devil definition and generates efficient low-level *** the Devil compiler checks safety critical properties, the long-awaited notion of robustness for hardware operating code is made possible. Finally, the wide variety of devices that we have already specified (mouse, sound, DMA, interrupt, Ethernet, video, and IDE disk controllers) demonstrates the expressiveness of the Devil language.
暂无评论