Efficiency image data retrieval based on asynchronous capability aware spatial search service middleware

2007 ◽  
Author(s):  
Nengcheng Chen ◽  
Zeqiang Chen ◽  
Jianya Gong
Keyword(s):  
Image Data ◽  
Data Retrieval ◽  
Spatial Search ◽  
Search Service

scholarly journals Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xiaoyue Cui
Keyword(s):  
Wavelet Transform ◽  
Image Data ◽  
Data Retrieval ◽  
Image Database ◽  
Fair Value ◽  
Visual Features ◽  
Retrieval Algorithm ◽  
Wavelet Filter ◽  
Database Retrieval ◽  
Retrieval Accuracy

Aiming at the problems of low image data retrieval accuracy and slow retrieval speed in the existing image database retrieval algorithms, this paper designs a clothing image database retrieval algorithm based on wavelet transform. Firstly, it represents the color consistency vector of clothing image, reflects the composition and distribution of image color through color histogram, quantifies the visual features of clothing image, aggregates them into a fixed size representation vector, and uses the Fair Value (FV) model to complete the collection of clothing image data. Then, the size of the clothing image is adjusted by using the size transformation technology, and the clothing pattern is divided into four moments with the same size. On this basis, the clothing image is discretized with the help of Hu invariant moment to complete the preprocessing of clothing image data. Finally, the generating function of wavelet transform is determined, and a cluster of functions is obtained through translation and expansion. The wavelet filter is decomposed into basic modules, and then, the wavelet transform is studied step by step. The clothing image data are regarded as a signal, split, predicted, and updated and input into the wavelet model, and the retrieval research of clothing image database is completed. The experimental results show that the design of the retrieval algorithm is reasonable, the retrieval data accuracy is high, and the retrieval speed is fast.

scholarly journals Exploring the Distribution Patterns of Flickr Photos

2019 ◽  
Vol 8 (9) ◽  
pp. 418 ◽  
Author(s):  
Ding ◽  
Fan
Keyword(s):  
Spatial Distribution ◽  
Image Data ◽  
Data Retrieval ◽  
Distribution Patterns ◽  
Quality Analysis ◽  
Distribution Analysis ◽  
Data Filtering ◽  
Data Source ◽  
Quality Assessments

In recent years, volunteered-geographic-information (VGI) image data have served as a data source for various geographic applications, attracting researchers to assess the quality of these images. However, these applications and quality assessments are generally focused on images associated with geolocation through textual annotations, which is only part of valid images to them. In this paper, we explore the distribution pattern for most relevant VGI images of specific landmarks to extend the current quality analysis, and to provide guidance for improving the data-retrieval process of geographic applications. Distribution is explored in terms of two aspects, namely, semantic distribution and spatial distribution. In this paper, the term semantic distribution is used to describe the matching of building-image tags and content with each other. There are three kinds of images (semantic-relevant and content-relevant, semantic-relevant but content-irrelevant, and semantic-irrelevant but content-relevant). Spatial distribution shows how relevant images are distributed around a landmark. The process of this work can be divided into three parts: data filtering, retrieval of relevant landmark images, and distribution analysis. For semantic distribution, statistical results show that an average of 60% of images tagged with the building’s name actually represents the building, while 69% of images depicting the building are not annotated with the building’s name. There was also an observation that for most landmarks, 97% of relevant building images were located within 300 m around the building in terms of spatial distribution.

scholarly journals A New Approach for Large-Scale Scene Image Retrieval Based on Improved Parallelk-Means Algorithm in MapReduce Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Jianfang Cao ◽  
Min Wang ◽  
Hao Shi ◽  
Guohua Hu ◽  
Yun Tian
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Image Data ◽  
Data Retrieval ◽  
Single Node ◽  
New Approach ◽  
Scene Image ◽  
Computational Performance ◽  
Traditional Image

The rapid growth of digital images has caused the traditional image retrieval technology to be faced with new challenge. In this paper we introduce a new approach for large-scale scene image retrieval to solve the problems of massive image processing using traditional image retrieval methods. First, we improved traditionalk-Means clustering algorithm, which optimized the selection of the initial cluster centers and iteration procedure. Second, we presented a parallel design and realization method for improvedk-Means algorithm applied it to feature clustering of scene images. Finally, a storage and retrieval scheme for large-scale scene images was put forward using the large storage capacity and powerful parallel computing ability of the Hadoop distributed platform. The experimental results demonstrated that the proposed method achieved good performance. Compared with the traditional algorithms with single node architecture and parallelk-Means algorithm, the proposed method has obvious advantages for use in large-scale scene image data retrieval in terms of retrieval accuracy, retrieval time overhead, and computational performance (speedup and efficiency, sizeup, and scaleup), which is a significant improvement from applying parallel processing to intelligent algorithms with large-scale datasets.

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