• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• Asia Marketing Journal
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• 제17권1호
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• pp.97-121
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• 2015

In this research, we examine whether and why temporal distance influences evaluations of two different types of brand extensions: concept-based extensions, defined as extensions primarily based on the importance or relevance of brand concepts to extension products; and similarity-based extensions, defined as extensions primarily based on the amount of feature similarity at the product-category level. In Study 1, we test the hypothesis that concept-based extensions are evaluated more favorably when they are framed to launch in the distant rather than in the near future, whereas similaritybased extensions are evaluated more favorably when they are framed to launch in the near rather than in the distant future. In Study 2, we confirm that this time-dependent differential evaluation is driven by the difference in construal level between the bases of the two types of extensions - i.e., brand-concept consistency and product-category feature similarity. As such, we find that conceptbased extensions are evaluated more favorably under the abstract than concrete mindset, whereas similarity-based extensions are evaluated more favorably under the concrete than abstract mindset. In Study 3, we extend to the case for a broad brand (i.e., brands that market products across multiple categories), finding that making accessible a specific product category of a broad parent brand influences evaluations of near-future, but not distant-future, brand extensions. Combined together, our findings suggest that temporal distance influences brand extension evaluation through its effect on the importance placed on brand concepts and feature similarity. That is, consumers rely on different bases to evaluate brand extensions, depending on their perception of when the extensions take place and on under what mindset they are placed. This research makes theoretical contributions to the brand extension research by identifying one important determinant to brand extension evaluation and also uncovering its underlying dynamics. It also contributes to expanding the scope of the construal level theory by putting forth a novel interpretation of two bases of perceived fit in terms of construal level. Marketers who are about to launch and advertise brand extensions may benefit by considering temporal-distance information in determining what content to deliver about extensions in their communication efforts. Conceptual relation of a parent brand to extensions needs to be emphasized in the distant future, whereas feature similarity should be highlighted in the near future.

• 한국공간정보시스템학회 논문지
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• 제9권1호
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• pp.59-77
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• 2007

모바일 환경의 대중화와 이를 위한 기반 기술의 발전으로 인하여 이동 객체들을 효과적으로 표현하고 분석하는 것이 중요한 문제로 대두되고 있다. 이러한 환경에서 이동 객체 궤적의 유사성 검색은 궤적에 대한 데이터 마이닝의 일부분으로 중요한 연구 분야중의 하나이다. 본 논문에서는 도로 네트워크상의 이동 객체 궤적을 위한 시공간 유사 궤적 검색 알고리즘을 제안한다. 이를 위하여 도로 네트워크상에서 두 이동 객체 궤적 사이의 시공간 거리를 정의하고, 이를 기반으로 궤적 사이의 시공간 유사도 측정 방법을 제안한다. 유사 궤적 알고리즘은 효율적인 검색을 위하여 시그니쳐 파일 기법을 이용하여 궤적을 검색한다. 마지막으로, 본 논문에서 제안하는 시공간 유사 궤적 검색 알고리즘을 구현하고, 성능 분석을 통해 제안하는 알고리즘의 효율성을 입증한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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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.

• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.37-43
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• 2016

This paper introduces a method for video shot group detection needed for efficient management and summary of video. The proposed method detects shots based on low-level visual properties and performs temporal and spatial clustering based on visual similarity of neighboring shots. Shot groups created from temporal clustering are further clustered into small groups with respect to visual similarity. A set of representative shot frames are selected from each cluster of the smaller groups representing a scene. Shots excluded from temporal clustering are also clustered into groups from which representative shot frames are selected. A number of video clips are collected and applied to the method for accuracy of shot group detection. We achieved 91% of accuracy of the method for shot group detection. The number of representative shot frames is reduced to 1/3 of the total shot frames. The experiment also shows the inverse relationship between accuracy and compression rate.

• 대한전자공학회논문지SP
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• 제41권3호
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• pp.129-137
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• 2004

이 논문은 Gabor 필터를 이용하여 두 서명 사이의 유사도를 계산하는 온라인 서명 검증 방법을 제안한다. 온라인 서명들의 유사도를 계산하기 위해서는 두 입력 서명의 각 위치 사이의 시간적인 대응 관계를 정확하게 구하는 것이 중요하다. 그러나 DP(dynamic programming) 매칭을 이용하는 기존의 포인트 매칭 방법은 시간이 많이 소요되는 단점이 있었다. 이 논문에서는 Gabor 필터의 위상 출력을 이용하여 두 서명 사이의 시간적인 대응 관계를 빠르게 추정하는 방법을 제안한다. 제안된 방법에서는 서명의 상이도로서 두 가지 척도를 정의한다. 첫째, 추정된 지역적 시간 변이로부터 두 서명사이의 시간적 상이도를 구하고, 둘째, 두 서명 특징 프로파일의 시간적 대응 관계를 이용하여 시간 정보가 보정된 특징 프로파일 상이도를 구한다. 제안된 방법은 고정된 길이의 코드로 코드화되어 기존의 DP 매칭을 사용하는 시간적 변이 추정 방법보다 30배 이상 빠른 속도로 서명을 비교할 수 있다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.429-432
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• 1999

Vegetation cover classification is examined based on a time series NOAA/AVHRR data. Time series data analysis methods including Fourier transform, Auto-Regressive (AR) model and temporal signature similarity matching are developed to extract phenological features of vegetation from a time series NDVI data from NOAA/AVHRR and to classify vegetation types. In the Fourier transform method, typical three spectral components expressing the phenological features of vegetation are selected for classification, and also in the AR model method AR coefficients are selected. In the temporal signature similarity matching method a new index evaluating the similarity of temporal pattern of the NDVI is introduced for classification.

• 한국통신학회논문지
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• 제22권6호
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• pp.1195-1209
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• 1997

This paper presents a new morphological spatio-temporal segmentation algorithm. The algorithm incorporates luminance and motion information simultaneously, and uses morphological tools such as morphological filtersand watershed algorithm. The procedure toward complete segmentation consists of three steps:joint marker extraction, boundary decision, and motion-based region fusion. First, the joint marker extraction identifies the presence of homogeneours regions in both motion and luminance, where a simple joint marker extraction technique is proposed. Second, the spatio-temporal boundaries are decided by the watershed algorithm. For this purposek, a new joint similarity measure is proposed. Finally, an elimination ofredundant regions is done using motion-based region function. By incorporating spatial and temporal information simultaneously, we can obtain visually meaningful segmentation results. Simulation results demonstratesthe efficiency of the proposed method.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.298-303
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• 2008

사용자의 과거 이동 경로 자료는 사용자의 현재 이동 위치를 예측하고 이외 관련된 서비스를 제공하는데 유용하게 사용될 수 있다. 본 논문에서는 사용자의 과거 이동 경로의 분석을 통하여 이동 중인 사용자의 시공간 위치예측 기술을 제안한다. 환경으로부터 발생한 사용자의 이동 경로를 수집하고 수집된 데이터에서 이동 경로 요약(Path Summarization)과 이동 경로 선택(Path Selection) 방법을 제안한다. 이동 경로 요약 방법은 환경으로부터 수집한 사용자의 이동 경로를 군집 분류하고, 이동 경로 선택 방법은 이동 중에 발생한 경로의 거리, 시간, 방향의 요소와 동적 정합법을 사용하여 유사성(Similarity)을 측정하며 유사성이 가장 높은 경로를 선택한다. 선택된 경로는 시간에 따른 공간 정보 빚 위치에 따른 시간 예측 서비스를 위하여 사용가능 하며, 실험을 통하여 유사성이 높은 이동 경로를 선택하는 모습을 확인하였다.