다중 질의를 위한 적응적 영상 내용 기반 검색 기법

홍종선,강대성,Hong Jong-Sun,Kang Dae-Seong 대한전자공학회 2005 電子工學會論文誌-SP (Signal processing) Vol.42 No.3

본 최근 영상 및 멀티미디어의 시각적인 내용을 기반으로 하는 검색 방법에 관한 많은 연구들이 진행되고 있다. 내용 기반 영상 검색(content-based image retrieval)에 관한 대부분의 기존의 질의 방법은 입력 영상에 의한 질의 또는 컬러(color), 형태(shape), 특징(texture) 등과 같은 low-level 특징을 사용한다. 그러나 이러한 방법들은 비교적 사용하기 불편하고 방법이 편중되어 있어서 일반 사용자들의 다양한 질의 요구에 적합하지 못하다. 본 논문에서 제안하는 것은 내용 기반 영상 검색 시스템 하의 컬러 객체의 자동 추출과 다중 질의를 위한 레이블링 알고리즘이다. 이것은 먼저 single colorizing 알고리즘을 사용하여 영상의 영역을 단순화 시키고 제안하는 Color and Spatial based Binary tree map (CSB tree map)을 이용하여 컬러 객체를 추출한다. 그리고 제안하는 레이블링 알고리즘을 이용하여 데이터베이스의 객체들을 색인한다. 이것은 컬러와 공간 정보를 고속으로 레이블링 하고 객체의 컬러 속성과 크기 및 위치 정보를 이용하여 객체의 컬러 기반과 공간적 기반의 조합을 바탕으로 하는 사용자의 다양한 질의에 부합할 수 있는 적응성 있는 시스템을 구현한다. 본 논문에서는 "Washington" 데이터베이스를 이용한 비교 실험을 통해서 제안하는 시스템의 검색 결과의 우수함을 알 수 있었다. Recently there have been many efforts to support searching and browsing based on the visual content of image and multimedia data. Most existing approaches to content-based image retrieval rely on query by example or user based low-level features such as color, shape, texture. But these methods of query are not easy to use and restrict. In this paper we propose a method for automatic color object extraction and labelling to support multiple queries of content-based image retrieval system. These approaches simplify the regions within images using single colorizing algorithm and extract color object using proposed Color and Spatial based Binary tree map(CSB tree map). And by searching over a large of number of processed regions, a index for the database is created by using proposed labelling method. This allows very fast indexing of the image by color contents of the images and spatial attributes. Futhermore, information about the labelled regions, such as the color set, size, and location, enables variable multiple queries that combine both color content and spatial relationships of regions. We proved our proposed system to be high performance through experiment comparable with another algorithm using 'Washington' image database.

내용 기반 음악 검색의 문제점 해결을 위한 전처리

정명범(Myoung-Beom Chung),성보경(Bo-Kyung Sung),고일주(Il-Ju Ko) 한국컴퓨터정보학회 2007 韓國컴퓨터情報學會論文誌 Vol.12 No.6

본 논문에서는 오디오를 내용기반으로 분석, 분류, 검색하기 위하여 사용되어 온 특징 추출 기법의 문제점을 제시하며, 새로운 검색 방법을 위해 하나의 전처리 과정을 제안한다. 기존 오디오 데이터 분석은 샘플링을 어떻게 하느냐에 따라 특징 값이 달라지기 때문에 같은 음악이라도 다른 음악으로 인식될 수 있는 문제를 갖고 있다. 따라서 본 논문에서는 다양한 포맷의 오디오 데이터를 내용 기반으로 검색하기 위해 PCM 데이터의 파형 정보 추출 방법을 제안한다. 이 방법을 이용하여 다양한 포맷으로 샘플링 된 오디오 데이터들이 같은 데이터임을 발견 할 수 있으며, 이는 내용기반 음악검색에 적용 할 수 있을 것이다. 이 방법의 유효성을 증명하기 위해 STFT를 이용한 특징 추출과 PCM 데이터의 파형 정보를 이용한 추출 실험을 하였으며, 그 결과 PCM 데이터의 파형 정보 추출 방법이 효과적임을 보였다. This paper presents the problem of the feature extraction techniques that has been used a content-based analysis. classification and retrieval in audio data and proposes a course of the preprocessing for a new contents-based retrieval methods. Because the feature vector according to sampling value changes. the existing audio data analysis is problem that same music is appraised by other music. Therefore. we propose waveform information extraction method of PCM data for retrieval audio data of various format to contents-based. If this method is used. we can find that audio datas that get into sampling in various format are same data. And it may be applied in contents-based music retrieval system. To verity the performance of the method. an experiment was done feature extraction using STFT and waveform information extraction using PCM data. As a result. we could know that the method to propose is effective more.

A New Content Based Image Retrieval System by HOG of Wavelet Sub Bands

Dr. Anna Saro Vijendran,S. Vinod Kumar 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.4

The term content-based image retrieval to describe his experiments into automatic retrieval of images from a database by color and shape feature. The term has since been widely used to describe the process of retrieving desired images from a large collection on the basis of features that can be automatically extracted from the images themselves. The features used for retrieval can be either primitive or semantic, but the extraction process must be predominantly automatic. Retrieval of images by manually-assigned keywords is definitely not CBIR as the term is generally understood – even if the keywords describe image content. And this paper deals, retrieve the images into three basic categories of color called RGB. After retrieving the image with their component the transformation can be applied. The HOG method is used to retrieve the feature of image vectors and others. In this paper, the HOG method is fully analyzed and proves its accuracy and efficiency of image retrieval with reduced number of steps.

Content-based image retrieval using a fusion of global and local features

부희형,김남철,김성호 한국전자통신연구원 2023 ETRI Journal Vol.45 No.3

Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content-based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.

An approach for improving the performance of the Content-Based Image Retrieval (CBIR)

정인성 한국측량학회 2012 한국측량학회지 Vol.30 No.6

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a ‘query’ image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper,experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

Content-based Image Retrieval Using Data Fusion Strategy

백우진,Sun-Eun Jung,Euigun Ahn,김기용,신문선 한국정보관리학회 2008 정보관리학회지 Vol.25 No.2

In many information retrieval experiments, the data fusion techniques have been used to achieve higher effectiveness in comparison to the single evidence-based retrieval. However, there had not been many image retrieval studies using the data fusion techniques especially in combining retrieval results based on multiple retrieval methods. In this paper, we describe how the image retrieval effectiveness can be improved by combining two sets of the retrieval results using the Sobel operator-based edge detection and the Self Organizing Map(SOM) algorithms. We used the clip art images from a commercial collection to develop a test data set. The main advantage of using this type of the data set was the clear cut relevance judgment, which did not require any human interven- tion.

Interactive Semantic Image Retrieval

Patil, Pushpa B.,Kokare, Manesh B. Korea Information Processing Society 2013 Journal of information processing systems Vol.9 No.3

The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval performance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.

Interactive Semantic Image Retrieval

( Pushpa B. Patil ),( Manesh B. Kokare ) 한국정보처리학회 2013 Journal of information processing systems Vol.9 No.3

The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval performance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.

Metadata Processing Technique for Similar Image Search of Mobile Platform

Seo, Jung-Hee The Korea Institute of Information and Commucation 2021 Journal of information and communication convergen Vol.19 No.1

Text-based image retrieval is not only cumbersome as it requires the manual input of keywords by the user, but is also limited in the semantic approach of keywords. However, content-based image retrieval enables visual processing by a computer to solve the problems of text retrieval more fundamentally. Vision applications such as extraction and mapping of image characteristics, require the processing of a large amount of data in a mobile environment, rendering efficient power consumption difficult. Hence, an effective image retrieval method on mobile platforms is proposed herein. To provide the visual meaning of keywords to be inserted into images, the efficiency of image retrieval is improved by extracting keywords of exchangeable image file format metadata from images retrieved through a content-based similar image retrieval method and then adding automatic keywords to images captured on mobile devices. Additionally, users can manually add or modify keywords to the image metadata.

Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias

Hwang Hye Jeon,Seo Joon Beom,Lee Sang Min,Kim Eun Young,Park Beomhee,Bae Hyun-Jin,Kim Namkug 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.2

Objective: To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). Materials and Methods: The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1–5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). Results: The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1–5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002). On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. Conclusion: The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.