4,062 research outputs found
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
Short user-generated videos classification using accompanied audio categories
This paper investigates the classification of short user-generated videos (UGVs) using the accompanied audio data since short UGVs accounts for a great proportion of the Internet UGVs and many short UGVs are accompanied by singlecategory soundtracks. We define seven types of UGVs corresponding to seven audio categories respectively. We also investigate three modeling approaches for audio feature representation, namely, single Gaussian (1G), Gaussian mixture (GMM) and Bag-of-Audio-Word (BoAW) models. Then using Support Vector Machine (SVM) with three different distance measurements corresponding to three feature representations, classifiers are trained to categorize the UGVs. The accompanying evaluation results show that these approaches are effective for categorizing the short UGVs based on their audio track. Experimental results show that a GMM representation with approximated Bhattacharyya distance (ABD) measurement produces the best performance, and BoAW representation with chi-square kernel also reports comparable results
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
Evaluating and combining digital video shot boundary detection algorithms
The development of standards for video encoding coupled with the increased power of computing mean that content-based manipulation of digital video information is now feasible. Shots are a basic structural building block of digital video and the boundaries between shots need to be determined automatically to allow for content-based manipulation. A shot can be thought of as continuous images from one camera at a time. In this paper we examine a variety of automatic techniques for shot boundary detection that we have implemented and evaluated on a baseline of 720,000 frames (8 hours) of broadcast television. This extends our previous work on evaluating a single technique based on comparing colour histograms. A description of each of our three methods currently working is given along with how they
are evaluated. It is found that although the different methods have about the same order of magnitude in terms of effectiveness, different shot boundaries are detected by the different methods. We then look at combining the three shot boundary detection methods to produce one output result and the benefits in accuracy and performance that this brought to our system. Each of the methods were changed from using
a static threshold value for three unconnected methods to one using three dynamic threshold values for one connected method. In a final summing up we look at the future directions for this work
Pandora: Description of a Painting Database for Art Movement Recognition with Baselines and Perspectives
To facilitate computer analysis of visual art, in the form of paintings, we
introduce Pandora (Paintings Dataset for Recognizing the Art movement)
database, a collection of digitized paintings labelled with respect to the
artistic movement. Noting that the set of databases available as benchmarks for
evaluation is highly reduced and most existing ones are limited in variability
and number of images, we propose a novel large scale dataset of digital
paintings. The database consists of more than 7700 images from 12 art
movements. Each genre is illustrated by a number of images varying from 250 to
nearly 1000. We investigate how local and global features and classification
systems are able to recognize the art movement. Our experimental results
suggest that accurate recognition is achievable by a combination of various
categories.To facilitate computer analysis of visual art, in the form of
paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art
movement) database, a collection of digitized paintings labelled with respect
to the artistic movement. Noting that the set of databases available as
benchmarks for evaluation is highly reduced and most existing ones are limited
in variability and number of images, we propose a novel large scale dataset of
digital paintings. The database consists of more than 7700 images from 12 art
movements. Each genre is illustrated by a number of images varying from 250 to
nearly 1000. We investigate how local and global features and classification
systems are able to recognize the art movement. Our experimental results
suggest that accurate recognition is achievable by a combination of various
categories.Comment: 11 pages, 1 figure, 6 table
Video genre categorization and representation using audio-visual information
International audienceWe propose an audio-visual approach to video genre classification using content descriptors that exploit audio, color, temporal, and contour information. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At the temporal structure level, we consider action content in relation to human perception. Color perception is quantified using statistics of color distribution, elementary hues, color properties, and relationships between colors. Further, we compute statistics of contour geometry and relationships. The main contribution of our work lies in harnessingn the descriptive power of the combination of these descriptors in genre classification. Validation was carried out on over 91 h of video footage encompassing 7 common video genres, yielding average precision and recall ratios of 87% to 100% and 77% to 100%, respectively, and an overall average correct classification of up to 97%. Also, experimental comparison as part of the MediaEval 2011 benchmarkingn campaign demonstrated the efficiency of the proposed audiovisual descriptors over other existing approaches. Finally, we discuss a 3-D video browsing platform that displays movies using efaturebased coordinates and thus regroups them according to genre
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