A FAST TWO-STAGE CONTENT-BASED IMAGE RETRIEVAL APPROACH IN THE DCT DOMAIN

2008 ◽  
Vol 22 (04) ◽  
pp. 765-781 ◽  
Author(s):  
TIENWEI TSAI ◽  
YO-PING HUANG ◽  
TE-WEI CHIANG
Keyword(s):  
Image Retrieval ◽  
High Efficiency ◽  
Feature Space ◽  
Content Based Image Retrieval ◽  
Two Stage ◽  
Feature Extraction Method ◽  
Domain Specific ◽  
Retrieval Scheme ◽  
Domain Specific Knowledge ◽  
Distinct Features

In this paper, a two-stage content-based image retrieval (CBIR) approach is proposed to improve the retrieval performance. To develop a general retrieval scheme which is less dependent on domain-specific knowledge, the discrete cosine transform (DCT) is employed as a feature extraction method. In establishing the database, the DC coefficients of Y, U and V components are quantized such that the feature space is partitioned into a finite number of grids, each of which is mapped to a grid code (GC). When querying an image, at coarse classification stage, the grid-based classification (GBC) and the distance threshold pruning (DTP) serve as a filter to remove those candidates with widely distinct features. At the fine classification stage, only the remaining candidates need to be computed for the detailed similarity comparison. The experimental results show that both high efficacy and high efficiency can be achieved simultaneously using the proposed two-stage approach.

scholarly journals Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

scholarly journals Feature space optimization for content-based image retrieval

2012 ◽  
Vol 12 (3) ◽  
pp. 7-19 ◽  
Author(s):  
Letricia P. S. Avalhais ◽  
Sergio F. da Silva ◽  
Jose F. Rodrigues ◽  
Agma J. M. Traina ◽  
Caetano Traina
Keyword(s):  
Image Retrieval ◽  
Feature Space ◽  
Content Based Image Retrieval

CONTENT-BASED IMAGE RETRIEVAL TRAINED BY ADABOOST FOR MOBILE APPLICATION

2006 ◽  
Vol 20 (04) ◽  
pp. 525-541 ◽  
Author(s):  
HWEI-JEN LIN ◽  
YANG-TA KAO ◽  
FU-WEN YANG ◽  
PATRICK S. P. WANG
Keyword(s):  
Image Retrieval ◽  
Mobile Devices ◽  
Mobile Application ◽  
Feature Space ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Training Process ◽  
Edge Information ◽  
Adaboost Algorithm ◽  
Wide Range

This paper proposes a Content-Based Image Retrieval (CBIR) system applicable in mobile devices. Due to the fact that different queries to a content-based image retrieval (CBIR) system emphasize different subsets of a large collection of features, most CBIR systems using only a few features are therefore only suitable for retrieving certain types of images. In this research we combine a wide range of features, including edge information, texture energy, and the HSV color distributions, forming a feature space of up to 1053 dimensions, in which the system can search for features most desired by the user. Through a training process using the AdaBoost algorithm9 our system can efficiently search for important features in a large set of features, as indicated by the user, and effectively retrieve the images according to these features. The characteristics of the system meet the requirements of mobile devices for performing image retrieval. The experimental results show that the performance of the proposed system is sufficiently applicable for mobile devices to retrieve images from a huge database.

A Content Based Image Retrieval Model Using Feature Space Dividing

2014 ◽  
Vol 596 ◽  
pp. 388-393
Author(s):  
Guan Huang
Keyword(s):  
Image Retrieval ◽  
Fractional Order ◽  
Feature Space ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Time Cost ◽  
Retrieval Model ◽  
Matching Accuracy ◽  
Query Image ◽  
Proposed Model

This paper introduces a model for content based image retrieval. The proposed model extracts image color, texture and shape as feature vectors; and then the image feature space is divided into a group of search zones; during the image searching phase, the fractional order distance is utilized to evaluate the similarity between images. As the query image vector only needs to compare with library image vectors located in the same search zone, the time cost is largely reduced. Further more the fractional order distance is utilized to improve the vector matching accuracy. The experimental results demonstrated that the proposed model provides more accurate retrieval results with less time cost compared with other methods.

scholarly journals Content Based Image Retrieval Based on Shape, Color and Structure of the Image

2019 ◽  
Vol 7 (3) ◽  
pp. 48-53
Author(s):  
T. Venkat Narayana Rao ◽  
Somasani Jyothi
Keyword(s):  
Social Media ◽  
Image Retrieval ◽  
High Efficiency ◽  
Content Based Image Retrieval ◽  
Data Bases ◽  
Huge Data ◽  
Large Databases ◽  
Color Texture

In the recent era, as technology is growing rapidly the usage of social media is also increasing as a result large databases are required for storing the images. With the advancements in the technology, the storage of these images in computers has become possible. But retrieving the images is becoming a big task. We need to store them in a sequential manner and retrieve them when required. This paper details retrieval of images by considering the features related to content like shape, color, texture is called CBIR (content based image retrieval). As it is very difficult to extract the pictures in such huge data bases so we chose this technique which aim at high efficiency.

scholarly journals Developing Forecasting Model in Thailand Fashion Market Based on Statistical Analysis and Content-Based Image Retrieval

2017 ◽  
pp. 159-173
Author(s):  
Komaek Kawinakrathiti ◽  
Suphakant Phimoltares ◽  
Patcha Utiswannakul
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Image Retrieval ◽  
High Efficiency ◽  
Content Based Image Retrieval ◽  
Fashion Industry ◽  
Fast Fashion ◽  
Internet Media ◽  
Analysis Technique ◽  
Trend Forecasting

Traditional trend forecasting process in Thailand fashion industry was challenged by a fast fashion. In this paper, the Content-Based Image Retrieval (CBIR) technique is utilized for retrieval of a fashion trendsetter in fast fashion influence. Firstly, six fashion theories were implemented as 12 variables affecting the trendsetter. Cluster analysis, and factor analysis approach were used to find out the source of a fashion trendsetter as well. Cluster analysis separated all samples into three groups with different fashion ways. Moreover, factor analysis technique grouped all variables into three important factors. From such techniques, Internet media clearly is the best source of a fashion trendsetter. In the authors' model, traditional forecasting sources were added up with a fast fashion influence from CBIR. Then, the CBIR was evaluated in terms of efficiency compared with a real fashion expert in the Thai fashion industry. From statistical test, spatial color distribution yields high efficiency in selecting similar fashion style as a fashion expert.

Sign in / Sign up

Export Citation Format

Share Document