37,293 research outputs found
An Information Theoretic Approach to Content Based Image Retrieval.
We propose an information theoretic approach to the representation and comparison of color features in digital images to handle various problems in the area of content-based image retrieval. The interpretation of color histograms as joint probability density functions enables the use of a wide range of concepts from information theory to be considered in the extraction of color features from images and the computation of similarity between pairs of images. The entropy of an image is a measure of the randomness of the color distribution in an image. Rather than replacing color histograms as an image representation, we demonstrate that image entropy can be used to augment color histograms for more efficient image retrieval. We propose an indexing algorithm in which image entropy is used to drastically reduce the search space for color histogram computations. Our experimental tests applied to an image database with 10,000 images suggest that the image entropy-based indexing algorithm is scalable for image retrieval of large image databases. We also proposed a new similarity measure called the maximum relative entropy measure for comparing image feature vectors that represent probability density functions. This measure is an improvement of the Kullback-Leibler number in that it is non-negative and satisfies the identity and symmetry axioms. We also propose a new usability paradigm called Query By Example Sets (QBES) that allows users, particularly novice users, the ability to express queries in terms of multiple images
On Archiving and Retrieval of Sequential Images From Tomographic Databases in PACS
In the picture archiving and communication systems (PACS) used in modern hospitals, the current practice is to retrieve images based on keyword search, which returns a complete set of images from the same scan. Both diagnostically useful and negligible images in the image databases are retrieved and browsed by the physicians. In addition to the text-based search query method, queries based on image contents and image examples have been developed and integrated into existing PACS systems. Most of the content-based image retrieval (CBIR) systems for medical image databases are designed to retrieve images individually. However, in a database of tomographic images, it is often diagnostically more useful to simultaneously retrieve multiple images that are closely related for various reasons, such as physiological continguousness, etc. For example, high resolution computed tomography (HRCT) images are taken in a series of cross-sectional slices of human body. Typically, several slices are relevant for making a diagnosis, requiring a PACS system that can retrieve a contiguous sequence of slices. In this paper, we present an extension to our physician-in-the-loop CBIR system, which allows our algorithms to automatically determine the number of adjoining images to retain after certain key images are identified by the physician. Only the key images, so identified by the physician, and the other adjoining images that cohere with the key images are kept on-line for fast retrieval; the rest of the images can be discarded if so desired. This results in large reduction in the amount of storage needed for fast retrieval
An Investigation on Text-Based Cross-Language Picture Retrieval Effectiveness through the Analysis of User Queries
Purpose: This paper describes a study of the queries generated from a user experiment for cross-language information retrieval (CLIR) from a historic image archive. Italian speaking users generated 618 queries for a set of known-item search tasks. The queries generated by user’s interaction with the system have been analysed and the results used to suggest recommendations for the future development of cross-language retrieval systems for digital image libraries.
Methodology: A controlled lab-based user study was carried out using a prototype Italian-English image retrieval system. Participants were asked to carry out searches for 16 images provided to them, a known-item search task. User’s interactions with the system were recorded and queries were analysed manually quantitatively and qualitatively.
Findings: Results highlight the diversity in requests for similar visual content and the weaknesses of Machine Translation for query translation. Through the manual translation of queries we show the benefits of using high-quality translation resources. The results show the individual characteristics of user’s whilst performing known-item searches and the overlap obtained between query terms and structured image captions, highlighting the use of user’s search terms for objects within the foreground of an image.
Limitations and Implications: This research looks in-depth into one case of interaction and one image repository. Despite this limitation, the discussed results are likely to be valid across other languages and image repository.
Value: The growing quantity of digital visual material in digital libraries offers the potential to apply techniques from CLIR to provide cross-language information access services. However, to develop effective systems requires studying user’s search behaviours, particularly in digital image libraries. The value of this paper is in the provision of empirical evidence to support recommendations for effective cross-language image retrieval system design.</p
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system
Region-Based Image Retrieval Revisited
Region-based image retrieval (RBIR) technique is revisited. In early attempts
at RBIR in the late 90s, researchers found many ways to specify region-based
queries and spatial relationships; however, the way to characterize the
regions, such as by using color histograms, were very poor at that time. Here,
we revisit RBIR by incorporating semantic specification of objects and
intuitive specification of spatial relationships. Our contributions are the
following. First, to support multiple aspects of semantic object specification
(category, instance, and attribute), we propose a multitask CNN feature that
allows us to use deep learning technique and to jointly handle multi-aspect
object specification. Second, to help users specify spatial relationships among
objects in an intuitive way, we propose recommendation techniques of spatial
relationships. In particular, by mining the search results, a system can
recommend feasible spatial relationships among the objects. The system also can
recommend likely spatial relationships by assigned object category names based
on language prior. Moreover, object-level inverted indexing supports very fast
shortlist generation, and re-ranking based on spatial constraints provides
users with instant RBIR experiences.Comment: To appear in ACM Multimedia 2017 (Oral
Interoperability between Multimedia Collections for Content and Metadata-Based Searching
Artiste is a European project developing a cross-collection search system for art galleries and museums. It combines image content retrieval with text based retrieval and uses RDF mappings in order to integrate diverse databases. The test sites of the Louvre, Victoria and Albert Museum, Uffizi Gallery and National Gallery London provide their own database schema for existing metadata, avoiding the need for migration to a common schema. The system will accept a query based on one museum’s fields and convert them, through an RDF mapping into a form suitable for querying the other collections. The nature of some of the image processing algorithms means that the system can be slow for some computations, so the system is session-based to allow the user to return to the results later. The system has been built within a J2EE/EJB framework, using the Jboss Enterprise Application Server
A Benchmark for Image Retrieval using Distributed Systems over the Internet: BIRDS-I
The performance of CBIR algorithms is usually measured on an isolated
workstation. In a real-world environment the algorithms would only constitute a
minor component among the many interacting components. The Internet
dramati-cally changes many of the usual assumptions about measuring CBIR
performance. Any CBIR benchmark should be designed from a networked systems
standpoint. These benchmarks typically introduce communication overhead because
the real systems they model are distributed applications. We present our
implementation of a client/server benchmark called BIRDS-I to measure image
retrieval performance over the Internet. It has been designed with the trend
toward the use of small personalized wireless systems in mind. Web-based CBIR
implies the use of heteroge-neous image sets, imposing certain constraints on
how the images are organized and the type of performance metrics applicable.
BIRDS-I only requires controlled human intervention for the compilation of the
image collection and none for the generation of ground truth in the measurement
of retrieval accuracy. Benchmark image collections need to be evolved
incrementally toward the storage of millions of images and that scaleup can
only be achieved through the use of computer-aided compilation. Finally, our
scoring metric introduces a tightly optimized image-ranking window.Comment: 24 pages, To appear in the Proc. SPIE Internet Imaging Conference
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