5145569
Automatic Ontology Derivation Using Clustering for Image Classification
Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval in the domain of image is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring that relevant information is not overlooked (high recall). The traditional solution to the problem of image retrieval employs contentbased search techniques based on color, texture or shape features. The traditional solution works well in performing searches in which the user specifies images containing a sample object, or a sample textural pattern, in which the object or pattern is indexed. One can overcome this restriction by indexing images according to meanings rather than objects that appear in images, although this will entail a way of converting objects to meanings. We have solved this problem of creating a meaning based index structure through the design and implementation of a concept-based model using domain dependent ontologies. An ontology is a collection of concepts and their interrelationships which provide an abstract view of an application domain. With regard to converting objects to meaning the key issue is to identify appropriate concepts that both describe and identify images. We propose a new mechanism that can generate ontologies automatically in order to make our approach scalable. To achieve this we propose a method for the automatic construction of ontologies based on clustering and a vector space model. Similarity of images is based on similarity of objects that appear in images. For object similarity measure, we consider the combination of color and shape similarity together.
29695672
Managing and Mining Multimedia Data, Animations and Annotations
Several advances have been made on managing multimedia data stores. We now have products for multimedia data management. This article provides an overview of managing multimedia databases and describes techniques for managing multimedia animations and annotations as well as mining multimedia data. 
 
 
Keywords: 
 
data mining; 
mulitimedia databases; 
animations; 
images; 
multimedia database; 
management system
46376386
Improving Image Annotations Using WordNet
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.
9818080
Automatic image annotation and retrieval using weighted feature selection
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose weighted feature selection algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented various different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation and retrieval.
17047961
Image annotations by combining multiple evidence & wordNet
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.
14052879
Automatic image annotation and retrieval using subspace clustering algorithm
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose a top-down, subspace clustering algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented four different models to link visual tokens with keywords based on the clustering results of our clustering algorithm and K-means algorithm, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that our algorithm is better than traditional ones for automatic image annotation and retrieval.
16763535
Handling Significant Scale Difference for Object Retrieval in a Supermarket
We propose an object retrieval application which can retrieve user specified objects from a big supermarket. Significant and unpredictable scale difference between the query and the database image is the major obstacle encountered. The widely used local invariant features show their deficiency in such an occasion. To improve the situation, we first design a new weighting scheme which can assess the repeatability of local features against scale variance. Also, another method which deals with scale difference through retrieving a query under multiple scales is also developed. Our methods have been tested on a real image database collected from a local supermarket and outperform the existing local invariant feature based image retrieval approaches. A new spatial check method is also briefly discussed.
17411195
Offer Group Generation and Delayed Processing in Multi-Issue Negotiation
In this paper we describe a mechanism for automated negotiation in which artificial agents can deal with a multi-issue negotiation in a one-to-many way based on some strategies. The negotiation process are governed dynamically by some strategies that emphasize on the diverse processing methods under the considering of different commodity attributes, including dynamic delayed evaluation and response to the received offers, generation of offer group with the same utility. Our analysis shows that the mechanism ensures that bargaining agents could reach a better agreement automatically. We also implement a prototype system to verify the advantages of our mechanism.
171642
Function Call Flow based Fitness Function Design in Evolutionary Testing
Evolutionary Testing has been shown a promising technology for the automatic test data generation. It reformulates test data generation as a metaheuristic search. A well- designed fitness function is essential to the efficiency of evolutionary search. Many efforts have been directed at the design and implementation of fitness function in recent years. However, previous work has just focused on the control dependency of the target on the branches in the same function. When function calls exist in the desired execution trace to the target, the evaluation of the test data on the coverage of these function calls, which should be provided to the evolutionary search, is not captured by the existing fitness function. In this case, the existing fitness function can not fairly evaluate the test data. And the evolutionary search will be hampered or even fail in severe cases. In this paper, a new term is first proposed to incorporate into the existing fitness function. It is applied to evaluate the test data's coverage of function calls along the desired path to the target. The new fitness function can evaluate the test data more fairly, resulting in a better guidance to the evolutionary search. This can be seen by the experiments carried out on two cases.
23948133
Building Domain Ontology Based on Web Data and Generic Ontology
The automatic or semi-automatic construction of ontology has become a research topic of interest in recent years. This paper describes a mechanism for constructing domain specific ontologies automatically based on web data and generic ontology. Firstly, we employ the hierarchical agglomerative clustering(HAC) algorithm, clustering web pages hierarchically and resulting in a binary tree. Then an algorithm is proposed, which selectd from the binary tree the significative nodes as topics implying concepts of domain interests. Lastly, the Chinese generic ontology, HowNet, is introduced to evolve the topics (together with their hierarchical structures) into domain ontology. We experiment our method in the field of computer hardware based on web pages collected from Chinese BtoC web sites. An in-depth discussion on the experiment results is also given.
13590609
Automatic Partitioning of Object-Oriented Programs for Resource-Constrained Mobile Devices with Multiple Distribution Objectives
We describe a system that takes monolithic Java programs as its input and automatically converts them into distributed Java programs. Our research is situated in the context of resource-constrained mobile devices, in which there are often multiple distribution objectives, such as minimizing energy consumption on mobile devices by off loading workloads to a stationary server, or minimizing total execution time. Our method initially constructs an object relation graph (ORG), using a combination of static analysis and offline profiling. Instead of directly partitioning this ORG, we then transform it into a target graph (TG) to abstract from concrete distribution infrastructures and objectives. By applying this two-layer graph modeling, we achieve a unified strategy for different partitioning goals. Preliminary benchmarks for our prototype implementation are highly promising, with an average speedup factor of almost 1.5 and an average energy savings of 83.5% for the beneficial benchmarks.