15759515
An Effective Evidence Theory Based K-Nearest Neighbor (KNN) Classification
In this paper, we study various K nearest neighbor (KNN) algorithms and present a new KNN algorithm based on evidence theory. We introduce global frequency estimation of prior probability (GE) and local frequency estimation of prior probability (LE). A GE for a class is the prior probability of the class across the whole training data space based on frequency estimation; on the other hand, a LE for a class in a particular neighborhood is the prior probability of the class in this neighborhood space based on frequency estimation. By considering the difference between the GE and the LE of each class, we present a solution to the imbalanced data problem in some degree without doing re-sampling. We compare our algorithm with other KNN algorithms using two benchmark datasets. Results show that our KNN algorithm outperforms other KNN algorithms, including basic evidence based KNN.
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.
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.
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.
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.
119959033
Breast mass segmentation based on information theory
In this study, an information-based algorithm, called c-shells based deterministic annealing (CSDA), is proposed for breast mass segmentation on digital mammograms. CSDA recasts the fuzzy clustering concept into the probability framework and offers two improved features over existing clustering algorithms. First, it is a global minimization algorithm through mass constrained deterministic annealing rather than a local minimization method in the original fuzzy c-shells (FCS) approach. Second, the prototype in this algorithm is shell, which is more effective in segmentation with compact or hollow spherical shells compared to the standard deterministic annealing (DA) algorithm. Experimental results show that the information based CSDA clustering algorithm is a promising image segmentation technique for digital mammographic mass detection.
34094601
DNA-Based Algorithm for 0-1 Planning Problem
Biochemical reaction theory based DNA computation is of much better performance in solving a class of intractable computational problems such as NP-complete problems, it is important to study the DNA computation. A novel algorithm based on DNA computation is proposed, which solves a special category of 0-1 planning problem by using the surface-based fluorescence labeling technique. The analysis show that our new algorithm is of significant advantages such as simple encoding, low cost and short operating time, etc.
35136972
Exact Best-Case End-to-End Response Time Analysis for Hard Real-Time Distributed Systems
The prediction of best-case response time is quite necessary to guarantee control accuracy and stability of distributed hard real-time systems. Published time analysis techniques can precisely dealt with the precedence relations between tasks on different processors, but usually ignored or inaccurately estimated the effects from the precedence relations between tasks on the same processor. This leads to a pessimistic analysis. This paper presents an algorithm to analyze the best-case end-to-end response time for hard real-time distributed systems. This algorithm extends the traditional analysis technique by exploiting the precedence relations between tasks on both different processors and the same processor and achieves a more accurate analysis. Simulation results have shown that this algorithm can achieve more accurate results than existing methods.
11066290
Robust multi-level video representation using mean shift analysis
A robust method for multi-level video representation based on the mean shift analysis (MSA) of low-level visual features is proposed in this paper. By tuning the bandwidth of MSA, video representation from the coarse level to the fine level can be achieved. This representation form provides a flexible scheme for content-based video analysis such as summarization, classification, and retrieval. Compared with the conventional k-means or fuzzy c-means algorithms, our method can adjust the resolution of representation in a more straightforward way, and is more robust since it does not need to initialize the cluster centers
10848491
Detecting and Eliminating Potential Violations of Sequential Consistency for Concurrent C/C++ Programs
When a concurrent shared-memory program written with a sequential consistency (SC) model is run on a machine implemented with a relaxed consistency (RC) model, it could cause SC violations that are very hard to debug. To avoid such violations, programmers need to provide explicit synchronizations or insert fence instructions. In this paper, we propose a scheme to detect and eliminate potential SC violations by combining Shasha/Snir's conflict graph and delay set theory with existing data race detection techniques. For each execution, we generate a race graph, which contains the improperly synchronized conflict accesses, called race accesses, and race cycles formed with those accesses. As a race cycle would probably lead to a non-sequential-consistent execution, we call it a potential violation of sequential consistency (PVSC) bug. We then compute the race delays of race cycles, and suggest programmers to insert fences into source code to eliminate PVSC bugs. We further convert a race graph into a PC race graph, and improves cycle detection and race delay computation to O(n^2), where n is the number of race access instructions. We evaluate our approach with the SPLASH-2 benchmarks, two large real-world applications (MySQL and Apache), and several multi-threaded Cilk programs. The results show that (1) the proposed approach could effec-tively detect PVSC bugs in real-world applications with good scalability; (2) it retains most of the performance of the concurrent program after inserting required fence instructions, with less than 6.3% performance loss; and (3) the additional cost of our approach over traditional race detection techniques is quite low, with 3.3% on average.