• ETRI Journal
• /
• v.33 no.5
• /
• pp.720-730
• /
• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

• Journal of Korea Multimedia Society
• /
• v.11 no.6
• /
• pp.777-786
• /
• 2008

Human face avatar is important information in nowadays, such as describing real people in virtual world. In this paper, we have presented a face avatar creation and warping algorithm by using face feature analysis method, in order to detect face feature, we utilized local mean method based on facial feature appearance and face geometric information. Then detect facial candidates by using it's character in $YC_bC_r$ color space. Meanwhile, we also defined the rules which are based on face geometric information to limit searching range. For analyzing face feature, we used face feature points to describe their feature, and analyzed geometry relationship of these feature points to create the face avatar. Then we have carried out simulation on PC and embed mobile device such as PDA and mobile phone to evaluate efficiency of the proposed algorithm. From the simulation results, we can confirm that our proposed algorithm will have an outstanding performance and it's execution speed can also be acceptable.

• Journal of Biomedical Engineering Research
• /
• v.31 no.6
• /
• pp.481-486
• /
• 2010

In this paper, we suggest an advanced algorithm, to recognize pedestrian/non-pedestrian using differential haar-like feature, which applies Adaboost algorithm to make a strong classification from weak classifications. First, we extract two feature vectors: horizontal haar-like feature and vertical haar-like feature. For the next, we calculate the proposed feature vector using differential haar-like method. And then, a strong classification needs to be obtained from weak classifications for composite recognition method using the differential area of horizontal and vertical haar-like. In the proposed method, we use one feature vector and one strong classification for the first stage of recognition. Based on our experiment, the proposed algorithm shows higher recognition rate compared to the traditional method for the pedestrian and non-pedestrian.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.5
• /
• pp.251-255
• /
• 2019

On today, Protocol reverse-engineering technique can be used to extract the specification of an unknown protocol. However, there is no standardized method, and in most cases, the extracting process is executed manually or semi-automatically. If the information about the structure of an unknown protocol could be acquired in advance, it would be easy to conduct reverse engineering. the feature extraction is an important step in unknown protocol classification. However, in this paper, we present a comparison several feature extraction techniques and suggests a method of feature extraction algorithm for recognizing unknown protocol. In order to verify the performance of the proposed system, we performed the training using eight open protocols to evaluate the performance using unknown data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.12
• /
• pp.6009-6027
• /
• 2019

Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.

• Korean Journal of Computational Design and Engineering
• /
• v.1 no.2
• /
• pp.122-132
• /
• 1996

Features are generic shapes with which engineers associate certain attributes and knowledge useful in reasoning about the product. Feature-based modeling systems support additional levels of information beyond those available in geometric modelers. The objective of this study is to develop a PC level feature-based modeling system which explicitly represents dimensions of the part. The feature-based modeler retains all the benefits of traditional B-rep. solid models, and represents the dimensions at a high level of a abstraction so that dimension driven geometry can be achieved.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1994.10a
• /
• pp.737-742
• /
• 1994

In this paper , we propose an incremental approach for recognizing a class of machining features from a featurebased design model as a part design proceeds, utilizing various information such as nominal geometry, design intents, and design feature characteristics. The proposed apptroach can handle complex intersecting features and protrusion features designed on oblique faces. The class of recognized volumetric machining features can be expressed as Material Removal Shape Element Volumes (MRSEVs), a PDES/STEP-based library of machining features.

• International Journal of CAD/CAM
• /
• v.5 no.1
• /
• pp.39-47
• /
• 2005

Different CAx systems are being utilized throughout the product lifecycle due to the practical reasons in the supply chain and design processes. One of the major problems facing enterprises of today is how to share and exchange data among heterogeneous applications. Since different software applications use different terminologies, it is difficult to share and exchange the product data with internal and external partners. This paper presents a method to enhance the CAD model interoperability based on feature ontology. The feature ontology has been constructed based on the feature definition of modeling commands of CAD systems. A method for integration of semantic data has been proposed, implemented, and tested with two commercial CAD systems.

• Korean Journal of Computational Design and Engineering
• /
• v.7 no.2
• /
• pp.121-130
• /
• 2002

The concept of Level Of Detail (LOD) was introduced and has been used to enhance display performance and to carry out certain engineering analysis effectively. We would like to use an adequate complexity level for each geometric model depending on specific engineering needs and purposes. Solid modeling systems are widely used in industry, and are applied to advanced applications such as virtual assembly. In addition, as the demand to share these engineering tasks through networks is emerging, the problem of building a solid model of an appropriate resolution to a given application becomes a matter of great necessity. However, current researches are mostly focused on triangular mesh models and various operators to reduce the number of triangles. So we are working on the multi-resolution of the solid model itself, rather than that of the triangular mesh model. In this paper, we propose multi-resolution representation of B-rep model by reordering and converting design features into an enclosing volume and subtractive features.

• Korean Journal of Computational Design and Engineering
• /
• v.1 no.3
• /
• pp.242-256
• /
• 1996

Although feature-based design is a promising approach to fully integrating CAD/CAM, current feature-based design approaches seldom provide methodologies to easily define and design features. This paper proposes a new approach to integrating parametric design with feature-based design to overcome those limitations by globally decomposing a design into a set of features and locally defining and positioning each feature by geometric constraints. Each feature is defined as a parametric shape which consists of a feature section, attributes, and a set of constraints. The generalized sketching and sweeping techniques are used to simplify the process of designing features. The proposed approach is knowledge-based and its computational efficiency in geometric reasoning is improved greatly. Parametrically designed features not only have the advantage of allowing users to efficiently perform design changes, but also provide designers with a natural design environment in which they can do their work more naturally and creatively.