• Title/Summary/Keyword: feature-based retrieval

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Audio Fingerprint Retrieval Method Based on Feature Dimension Reduction and Feature Combination

  • Zhang, Qiu-yu;Xu, Fu-jiu;Bai, Jian
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.2
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    • pp.522-539
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    • 2021
  • In order to solve the problems of the existing audio fingerprint method when extracting audio fingerprints from long speech segments, such as too large fingerprint dimension, poor robustness, and low retrieval accuracy and efficiency, a robust audio fingerprint retrieval method based on feature dimension reduction and feature combination is proposed. Firstly, the Mel-frequency cepstral coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) of the original speech are extracted respectively, and the MFCC feature matrix and LPCC feature matrix are combined. Secondly, the feature dimension reduction method based on information entropy is used for column dimension reduction, and the feature matrix after dimension reduction is used for row dimension reduction based on energy feature dimension reduction method. Finally, the audio fingerprint is constructed by using the feature combination matrix after dimension reduction. When speech's user retrieval, the normalized Hamming distance algorithm is used for matching retrieval. Experiment results show that the proposed method has smaller audio fingerprint dimension and better robustness for long speech segments, and has higher retrieval efficiency while maintaining a higher recall rate and precision rate.

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

  • Jeong, Inseong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.30 no.6_2
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    • pp.665-672
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    • 2012
  • 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.

A METHOD OF IMAGE DATA RETRIEVAL BASED ON SELF-ORGANIZING MAPS

  • Lee, Mal-Rey;Oh, Jong-Chul
    • Journal of applied mathematics & informatics
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    • v.9 no.2
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    • pp.793-806
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    • 2002
  • Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps (SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

Feature-Based Image Retrieval using SOM-Based R*-Tree

  • Shin, Min-Hwa;Kwon, Chang-Hee;Bae, Sang-Hyun
    • Proceedings of the KAIS Fall Conference
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    • 2003.11a
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    • pp.223-230
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    • 2003
  • Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R*-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R*-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R*-tree combines SOM and R*-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R*-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R*-tree with that of an SOM and an R*-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R*-tree outperforms both the SOM and R*-tree due to the reduction of the number of nodes required to build R*-tree and retrieval time cost.

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Content-Based Image Retrieval Algorithm Using HAQ Algorithm and Moment-Based Feature (HAQ 알고리즘과 Moment 기반 특징을 이용한 내용 기반 영상 검색 알고리즘)

  • 김대일;강대성
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.4
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    • pp.113-120
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    • 2004
  • In this paper, we propose an efficient feature extraction and image retrieval algorithm for content-based retrieval method. First, we extract the object using Gaussian edge detector for input image which is key frames of MPEG video and extract the object features that are location feature, distributed dimension feature and invariant moments feature. Next, we extract the characteristic color feature using the proposed HAQ(Histogram Analysis md Quantization) algorithm. Finally, we implement an retrieval of four features in sequence with the proposed matching method for query image which is a shot frame except the key frames of MPEG video. The purpose of this paper is to propose the novel content-based image retrieval algerian which retrieves the key frame in the shot boundary of MPEG video belonging to the scene requested by user. The experimental results show an efficient retrieval for 836 sample images in 10 music videos using the proposed algorithm.

The Design of Adaptive Component Analysis System for Image Retrieval (영상 검색을 위한 적응적 컴포넌트 분석 시스템 설계)

  • 최철;박장춘
    • Journal of the Korea Society of Computer and Information
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    • v.9 no.2
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    • pp.19-26
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    • 2004
  • This paper proposes ACA (Adaptive Component Analysis) as a method for feature extraction and analysis of the content-based image retrieval system. For satisfactory retrieval, the features extracted from images should be appropriately applied according to the image domains and for this, retrieval measurement is proposed in this study. Retrieval measurement is a standard indicating how important the value of a relevant feature is to image retrieval. ACA is a middle stage for content-based image retrieval and it purposes to improve the retrieval speed and performance.

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The Design of Adaptive Component Analysis System for Image Retrieval (영상 검색을 위한 적응적 컴포넌트 분석 시스템 설계)

  • 최철;박장춘
    • KSCI Review
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    • v.12 no.1
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    • pp.9-19
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    • 2004
  • This paper proposes ACA (Adaptive Component Analysis) as a method for feature extraction and analysis of the content-based image retrieval system. For satisfactory retrieval, the features extracted from images should be appropriately applied according to the image domains and for this. retrieval measurement is Proposed in this study. Retrieval measurement is a standard indicating how important the value of a relevant feature is to image retrieval ACA is a middle stage for content-based image retrieval and it purposes to improve the retrieval speed and performance.

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A Semantics-based Video Retrieval System using Annotation and Feature (주석 및 특징을 이용한 의미기반 비디오 검색 시스템)

  • 이종희
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.41 no.4
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    • pp.95-102
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    • 2004
  • In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency md requires many efforts of system administrator or annotator because of imperfect automatic processing. In this paper, we propose semantics-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method and optimized comparison area extracting that propose. Therefore, we propose the system that can heighten retrieval efficiency of video data through semantics-based retrieval.

Content-Based Image Retrieval System using Feature Extraction of Image Objects (영상 객체의 특징 추출을 이용한 내용 기반 영상 검색 시스템)

  • Jung Seh-Hwan;Seo Kwang-Kyu
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.27 no.3
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    • pp.59-65
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    • 2004
  • This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

Region Division for Large-scale Image Retrieval

  • Rao, Yunbo;Liu, Wei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.10
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    • pp.5197-5218
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    • 2019
  • Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.