• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.631-644
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• 2002

In this paper efficient algorithms for content-based video retrieval using motion information are proposed, including temporal scale-invariant retrieval and temporal scale-absolute retrieval. In temporal scale-invariant video retrieval, the distance transformation is performed on each trail image in database. Then, from a given que교 trail the pixel values along the query trail are added in each distance image to compute the average distance between the trails of query image and database image, since the intensity of each pixel in distance image represents the distance from that pixel to the nearest edge pixel. For temporal scale-absolute retrieval, a new coding scheme referred to as Motion Retrieval Code is proposed. This code is designed to represent object motions in the human visual sense so that the retrieval performance can be improved. The proposed coding scheme can also achieve a fast matching, since the similarity between two motion vectors can be computed by simple bit operations. The efficiencies of the proposed methods are shown by experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1756-1776
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• 2012

Wireless sensor networks (WSNs) are likely to be more prevalent as their cost-effectiveness improves. The spectrum of applications for WSNs spans multiple domains. In environmental sciences, in particular, they are on the way to become an essential technology for monitoring the natural environment and the dynamic behavior of transient physical phenomena over space. Existing sensor network query processors (SNQPs) have also demonstrated that in-network processing is an effective and efficient means of interaction with WSNs for performing queries over live data. Inspired by these findings, this paper investigates the question as to whether spatio-temporal and historical analysis can be carried over WSNs using distributed query-processing techniques. The emphasis of this work is on the spatial, temporal and historical aspects of sensed data, which are not adequately addressed in existing SNQPs. This paper surveys the novel approaches of storing the data and execution of spatio-temporal and historical queries. We introduce the challenges and opportunities of research in the field of in-network storage and in-network spatio-temporal query processing as well as illustrate the current status of research in this field. We also present new areas where the spatio-temporal and historical query processing can be of significant importance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• Spatial Information Research
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• v.10 no.2
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• pp.185-199
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• 2002

The objective of this study is to investigate the suitable spatio-temporal analysis method considering the zoning effect of spatial analysis termed the modifiable areal unit problem(MAUP). In former studies of spatio-temporal analysis, there were disagreement between attribute data with spatial data, because of variation of administrative district aggregating attribute data. It is need to consider how the analysis zone effects spatial characteristics and spatio-temporal variation of urban region through land price variation analysis. This study considers MAUP through basic mesh system, which is composed of micro grid. Mesh system can solve disagreement of resolution between spatial data and attribute data.

• Journal of KIISE:Software and Applications
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• v.33 no.3
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• pp.356-364
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• 2006

'Ubiquitous Computing' is the most important paradigm of the next generation Information-Communication technology. The one of important problems to develop ubiquitous computing service system get hold of relations between times of transfer objects and events of transfer objects. Another problem is what reason transfer-pattern through location data of transfer objects. In this paper, we propose an approach to offer temporal-relation service in ubiquitous computing environment. The first is temporal reasoning in service viewpoint. The second is temporal reasoning to record user's preference. Users have preferences that are closely connected with time. These preferences are recorded at user profile. Therefore, the user profile-based ubiquitous service system can offer suitable service to users.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.67-80
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• 2017

This paper presents a hole-filling method using extrapolated spatio-temporal background information to obtain a synthesized view. A new temporal background model using non-overlapped patch based background codebook is introduced to extrapolate temporal background information In addition, a depth-map driven spatial local background estimation is addressed to define spatial background constraints that represent the lower and upper bounds of a background candidate. Background holes are filled by comparing the similarities between the temporal background information and the spatial background constraints. Additionally, a depth map-based ghost removal filter is described to solve the problem of the non-fit between a color image and the corresponding depth map of a virtual view after 3-D warping. Finally, an inpainting is applied to fill in the remaining holes with the priority function that includes a new depth term. The experimental results demonstrated that the proposed method led to results that promised subjective and objective improvement over the state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2468-2487
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• 2015

In web searching, trustable and precise results are greatly affected by the inherent uncertainty in the input queries. Queries submitted to search engines are by nature ambiguous and constitute a significant proportion of the instances given to web search engines. Ambiguous queries pose real challenges for the web search engines due to versatility of information. Temporal based approaches whereas somehow reduce the uncertainty in queries but still lack to provide results according to users aspirations. Web search science has created an interest for the researchers to incorporate contextual information for resolving the uncertainty in search results. In this paper, we propose an Adaptive Disambiguation Approach (ADA) of hybrid nature that makes use of both the temporal and contextual information to improve user experience. The proposed hybrid approach presents the search results to the users based on their location and temporal information. A Java based prototype of the systems is developed and evaluated using standard dataset to determine its efficacy in terms of precision, accuracy, recall, and F1-measure. Supported by experimental results, ADA demonstrates better results along all the axes as compared to temporal based approaches.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• The Transactions of the Korea Information Processing Society
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• v.1 no.2
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• pp.163-171
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• 1994

The paper describes a design and implementation of a user interface system between a temporal database management system (DBMS) and a graphical display system. This interfacing system consists of a temporal DBMS, a graphical display system, and an interface control system. The temporal DBMS supports time and the graphical display system draws a graphic using an icon. The interfacing system shows the graphical animation with time factors by using a graphical knowledgeable. We describe how to interface them as well as the structure of temporal DBMS and graphical display system.