• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권3호
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• pp.522-538
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• 2013

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권11호
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• pp.1519-1533
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• 2004

웹 기반의 학술분야 전문 검색 시스템은 사용자의 정보 요구 표현을 극히 제한적으로 허용함으로써 검색된 정보의 내용 분석과 정보 습득의 과정이 일관되지 못해 무분별한 정보 제공이 이루어진다. 본 논문에서는 용어의 상대적인 중요 정도를 축소용어 집합으로 구성하여 검색 시스템의 높은 시간 복잡도를 해결할 수 있도록 퍼지 검색 모델을 적용하였다. 또한 퍼지 호환관계의 특성을 만족하는 유사관계 행렬을 통해 사용자 질의를 정확하게 반영할 수 있도록 클러스터 검색을 수행하였다. 본 논문에서 제안한 퍼지 검색과 문서 클러스터 검색의 유사도 결합을 통한 순위 재조정 검색 모델은 검색 성능을 표현하는 정확률과 재현율 척도에서 향상됨을 입증하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권4호
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• pp.324-328
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• 2012

With the recent advances in information retrieval, cross-media retrieval has been attracting lot of attention, but several issues remain problems such as constructing effective correlations, calculating similarity between different kinds of media objects. To gain better cross-media retrieval performance, it is crucial to mine the semantic correlations among the heterogeneous multimedia data. This paper introduces a new method for cross-media retrieval which uses ontology to organize different media objects. The experiment results show that the proposed method is effective in cross-media retrieval.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제16권8호
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• pp.837-841
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• 2010

본 논문에서는 MPEG-7 DCD를 이용하여 내용기반 이미지 검색을 할 때 적합한 유사도 측정 방법을 제안한다. 제안한 방법은 이미지에서 추출한 도미넌트 컬러의 비율에 따라 유사도를 측정할 수 있도록 하였다. 실험결과 제안한 방법은 MPEG-7 DCD의 QHDM[1]에 의한 검색결과보다 전역 DCD를 사용할 경우 ANMRR이 18.9%의 성능향상을 보였으며 블록별 DCD를 사용할 경우 47.2%라는 높은 성능향상을 보였다. 이는 제안한 방법이 DCD를 이용하여 내용기반 이미지 검색을 할 때 효과적인 유사도 측정 방법임을 보여준다. 특히, 영역 기반의 이미지 검색 방법에 유용하게 적용할 수 있을 것으로 보인다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1997년도 Proceedings International Workshop on New Video Media Technology
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• pp.137-142
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• 1997

One of the inherent problems in constructing the sign language dictionary is how to make retrieval and comparison operations on the visual database of signs. This paper describes a retrieval method, especifically for Japanese signs. This method has a useful capability for flexible retrieval of the sign from a bilingual dictionary. Our method can retrieve similar signs to the given input. The retrieval mechanism is essentially based on similarity between the given verbal description and verbal descriptions in a retrieval database. The similarity measure of verbal descriptions can be considered as the approximations for the similarity of sign motion images. As a results of our experiment, the success ratio of the retrievals is 96% in averages.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• 한국멀티미디어학회논문지
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• 제7권5호
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• pp.617-632
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• 2004

비디오 데이터에는 시간의 변화에 따라 공간적인 위치가 변화하는 움직임 객체를 포함하고 있다. 이 논문에서는 비디오 데이터의 움직임 객체에 대한 새로운 모델링 방법을 제안한다. 제안하는 모델링은 움직임 객체를 효과적으로 검색하기 위해 시간의 변화에 따라 공간적인 위치와 크기 변화를 표현한다. 또한 객체의 시간에 따른 시각적 특징 변화와 객체의 방향, 거리 그리고 속도를 고려한 궤적을 표현한다. 따라서 움직임 객체의 시각적인 특징 유사도 검색, 거리 유사도 검색, 제적 유사도 검색을 수행할 수 있다. 또한 이들을 통합한 가중치 검색이 가능하도록 한다.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.32-55
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• 2018

The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2527-2545
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• 2016

Measuring the similarity of given samples is a key problem of recognition, clustering, retrieval and related applications. A number of works, e.g. kernel method and metric learning, have been contributed to this problem. The challenge of similarity learning is to find a similarity robust to intra-class variance and simultaneously selective to inter-class characteristic. We observed that, the similarity measure can be improved if the data distribution and hidden semantic information are exploited in a more sophisticated way. In this paper, we propose a similarity learning approach for retrieval and recognition. The approach, termed as LDA-FEK, derives free energy kernel (FEK) from Latent Dirichlet Allocation (LDA). First, it trains LDA and constructs kernel using the parameters and variables of the trained model. Then, the unknown kernel parameters are learned by a discriminative learning approach. The main contributions of the proposed method are twofold: (1) the method is computationally efficient and scalable since the parameters in kernel are determined in a staged way; (2) the method exploits data distribution and semantic level hidden information by means of LDA. To evaluate the performance of LDA-FEK, we apply it for image retrieval over two data sets and for text categorization on four popular data sets. The results show the competitive performance of our method.