12 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
xi
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
Semantic-based technique for thai documents plagiarism detection
Plagiarism is the act of taking another person's writing or idea without referring to the source of information. This is one of major problems in educational institutes. There is a number of plagiarism detection software available on the Internet. However, a few numbers of them works. Typically, they use a simple method for plagiarism detection e.g. string matching. The main weakness of this method is it cannot detect the plagiarism when the author replaces some words using synonyms. As such, this paper presents a new technique for a semantic-based plagiarism detection using Semantic Role Labeling (SRL) and term weighting. SRL is deployed in order to calculate the semantic-based similarity. The main different from the existing framework is terms in a sentence are weighted dynamically depending on their roles in the sentence e.g. subject, verb or object. This technique enhances the plagiarism detection mechanism more efficiently than existing system although positions of terms in a sentence are reordered. The experimental results show that the proposed method can detect the plagiarism document more effective than the existing methods, Anti-kobpae, Turnit-in and Traditional Semantic Role Labeling
Data from: Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas
Background: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly, and thus, they cannot enhance the capability of the predictive model’s learning algorithm. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings: We identified the study areas in three provinces (Nakhon Pathom, Ratchaburi, and Samut Sakhon) of central Thailand that were reported to have a high incidence of dengue outbreaks. Prior to being added to the model, the infection data of the dengue vector, Aedes aegypti, the climate parameters, and the population density were collected from various sources and standardized. This process ensured that the data were not overwhelmed by each other in terms of the distance measures and to enhance the model effectiveness. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Aedes aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. The support vector machine (SVM), a machine learning technique, has been receiving attention in many research areas due to its remarkable generalization performance. In this research, we applied the SVM with the radial basis function (RBF) kernel, referred to as the SVM-R, to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions: The infection rates of the Aedes aegypti female mosq uitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. This approach is more reliable and practical for monitoring dengue outbreaks, particularly in locations with similar climates because it does not rely on only climate factors. In addition, we demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE)
PONE-D-14-48810
Raw data for PLOS ONE journal entitle: Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical area
Prediction performance comparison based on the maximum, average, and minimum accuracy of the six models compared with the accuracy of the training set.
<p>The experiment was conducted 10 times using 10-fold cross-validation performed on the training set and the test set data using the SVM-L, SVM-P, SVM-R, NN, DT, and <i>KNN</i> techniques. All the collected results were averaged.</p
Independent and dependent variables collected from various sources and used in the proposed forecasting model.
<p>Independent and dependent variables collected from various sources and used in the proposed forecasting model.</p
The trends of four predictors, dengue cases, and the infection rates of female, male, and larvae mosquitoes, are plotted and compared.
<p>This figure illustrates the trends of four parameters in Nakhon Pathom. The trends of these parameters in the other two provinces (Ratchaburi and Samut Sakhon) are similar but are not shown due to space limitations.</p
The prediction performance comparison based on the MAE and forecasting accuracy.
<p>Fig 3 (A) shows the MAE and the prediction accuracy with varying values of <i>σ</i><sup>2</sup> while C was fixed. In contrast, the value of C was varied and <i>σ</i><sup>2</sup> was fixed in Fig 3 (B) to determine the optimal values for the MAE and prediction accuracy.</p