7,195 research outputs found
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
Integrating Perceptual Signal Features within a Multi-facetted Conceptual Model for Automatic Image Retrieval
International audienceThe majority of the content-based image retrieval (CBIR) systems are restricted to the representation of signal aspects, e.g. color, texture... without explicitly considering the semantic content of images. According to these approaches a sun, for example, is represented by an orange or yellow circle, but not by the term "sun". The signal-oriented solutions are fully automatic, and thus easily usable on substantial amounts of data, but they do not fill the existing gap between the extracted low-level features and semantic descriptions. This obviously penalizes qualitative and quantitative performances in terms of recall and precision, and therefore users' satisfaction. Another class of methods, which were tested within the framework of the Fermi-GC project, consisted in modeling the content of images following a sharp process of human-assisted indexing. This approach, based on an elaborate model of representation (the conceptual graph formalism) provides satisfactory results during the retrieval phase but is not easily usable on large collections of images because of the necessary human intervention required for indexing. The contribution of this paper is twofold: in order to achieve more efficiency as far as user interaction is concerned, we propose to highlight a bond between these two classes of image retrieval systems and integrate signal and semantic features within a unified conceptual framework. Then, as opposed to state-of-the-art relevance feedback systems dealing with this integration, we propose a representation formalism supporting this integration which allows us to specify a rich query language combining both semantic and signal characterizations. We will validate our approach through quantitative (recall-precision curves) evaluations
Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos
We propose a new zero-shot Event Detection method by Multi-modal
Distributional Semantic embedding of videos. Our model embeds object and action
concepts as well as other available modalities from videos into a
distributional semantic space. To our knowledge, this is the first Zero-Shot
event detection model that is built on top of distributional semantics and
extends it in the following directions: (a) semantic embedding of multimodal
information in videos (with focus on the visual modalities), (b) automatically
determining relevance of concepts/attributes to a free text query, which could
be useful for other applications, and (c) retrieving videos by free text event
query (e.g., "changing a vehicle tire") based on their content. We embed videos
into a distributional semantic space and then measure the similarity between
videos and the event query in a free text form. We validated our method on the
large TRECVID MED (Multimedia Event Detection) challenge. Using only the event
title as a query, our method outperformed the state-of-the-art that uses big
descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC
metric. It is also an order of magnitude faster.Comment: To appear in AAAI 201
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
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