A Novel and Fast Method for Content-Based Image Retrieval via Stack-Triangles Geometrics Histogram

2011 ◽  
Vol 341-342 ◽  
pp. 785-789
Author(s):  
Yu Feng Chen ◽  
Chuan Qi Tan ◽  
Feng Xia Li ◽  
Qing Yi Zhang
Keyword(s):  
Image Retrieval ◽  
Open Problem ◽  
Shape Matching ◽  
New Method ◽  
Content Based Image Retrieval ◽  
Good Effect ◽  
Fast Method ◽  
Novel Method

In this paper, a novel method is proposed for solving an open problem of shape matching in content-based image retrieval. Our method regards the stack-triangles geometrics histogram to be the feature of an image, the correlation of two images was decided by the compare result of their stack-triangles geometrics histograms. First of all, these methods extract the key contour fragments of an image, so that a shape can be presented as many key contour fragments in this way. In addition, we derivation stack triangles of a shape by the key contour fragments we get it before. Then, we can get the stack-triangles geometrics histogram of the image and calculate the correlation of two images use their stack-triangles geometrics histogram. Finally, experiment results show that our new method is a very fast method and can get more robustness than existing methods and achieve a good effect in the standard shape databases MPEG-7.

A New Method for Content-Based Image Retrieval via Subsets of Key Contours Fragments

2012 ◽  
Vol 532-533 ◽  
pp. 792-796
Author(s):  
Chuan Qi Tan ◽  
Yu Feng Chen ◽  
Feng Xia Li ◽  
Zhi Zhong Yang
Keyword(s):  
Image Retrieval ◽  
Open Problem ◽  
Shape Matching ◽  
New Method ◽  
Content Based Image Retrieval ◽  
Similar Measure ◽  
Matching Effect ◽  
Image Contour ◽  
Novel Method ◽  
Correlation Rate

In this paper, a novel method is proposed for solving an open problem of shape matching in content-based image retrieval. In order to prepare for matching two images, our novel method uses an improved segment method to get more accurate contours. And then we find a new way to extract the key contour fragments, so that a shape can be presented as many key contour fragments in this way. It is an effective way to do analysis in image contour. In addition, a similar measure method is proposed to get the correlation rate of two images via subsets of key contour fragments. Finally, experiment results show that our new method can get more robustness than existing methods and achieve a superior matching effect in the standard shape databases MPEG-7.

CONTENT-BASED IMAGE RETRIEVAL OF KAOU IMAGES BY RELAXATION MATCHING OF REGION FEATURES

2006 ◽  
Vol 14 (04) ◽  
pp. 509-523 ◽  
Author(s):  
KEISUKE KAMEYAMA ◽  
SOO-NYOUN KIM ◽  
MICHITERU SUZUKI ◽  
KAZUO TORAICHI ◽  
TAKASHI YAMAMOTO
Keyword(s):  
Image Retrieval ◽  
Chinese Character ◽  
Distance Functions ◽  
Content Based Image Retrieval ◽  
Historical Documents ◽  
Image Modeling ◽  
Weighted Sum ◽  
Old Japanese ◽  
Novel Method ◽  
Relaxation Matching

An improvement to the content-based image retrieval (CBIR) system for kaou images which has been developed by the authors group is introduced. Kaous are handwritten monograms found on old Japanese documents in a Chinese character-like shapes with artistic decorations. Kaous play an important role in the research of historical documents, which involve browsing and comparison of numerous samples. In this work, a novel method of kaou image modeling for CBIR is introduced, which incorporates the shade information of a closed kaou region in addition to the conventionally used contour characteristics. Dissimilarity of query and dictionary images were calculated as a weighted sum of elementary differences in the positions, contour shapes and colors of the component regions. These elementary differences were evaluated using relaxation matching and empirically defined distance functions. In the experiments, a set of 2455 kaou images were used. It was found that apparently similar kaou images could be retrieved by the proposed method, improving the retrieval quality. .

A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

2018 ◽  
Vol 45 (1) ◽  
pp. 117-135 ◽  
Author(s):  
Amna Sarwar ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
Khurram Ashfaq Qazi ◽  
Ahmed Adnan ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Image Databases ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Bag Of Words ◽  
Visual Words ◽  
Visual Attributes ◽  
Visual Vocabulary ◽  
Novel Method

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

Research on Related Technology of Content-Based Image Retrieval

2013 ◽  
Vol 448-453 ◽  
pp. 3616-3620
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Bai Chuan Li
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
The Novel ◽  
Effective Solution ◽  
Retrieval Accuracy ◽  
Novel Method ◽  
Feedback Method ◽  
High Level

Content-Based Image Retrieval (CBIR) system existed a gap between high-level concepts and low-level features. As an effective solution, the Relevance Feedback (RF) technique has been used on many CBIR systems to improve the retrieval precision. In order to further improve convergence speed and retrieval accuracy, a novel relevance feedback method was proposed. According to feedback from user, image feature was weighted and adjusted in the novel method.

Content-Based Image Retrieval through Region Uniformly Partition

2012 ◽  
Vol 500 ◽  
pp. 471-474 ◽  
Author(s):  
Xiao Xiao ◽  
De Wen Zhuang ◽  
Shou Jue Wang
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Similarity Measure ◽  
Open Problem ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Content Based Image Retrieval ◽  
Retrieval Performance ◽  
Color Features ◽  
Better Than

It has been demonstrated that accurate image segmentation is still an open problem. For avoiding this difficulties in content-based image retrieval, an region uniform partition approaching was proposed. Based on fusing regional color features using smooth slide histogram and texture features extracted using Gabor wavelet, we provided the corresponding similarity measure. The image retrieval performance on a subset of the COREL database are better than SIMPLIcity system showed the effectiveness of the proposed method.

Shape-matching approach to content-based image retrieval

1999 ◽  
Author(s):  
JongSeung Park ◽  
HwangSeok Oh ◽  
Duk-Ho Chang
Keyword(s):  
Image Retrieval ◽  
Shape Matching ◽  
Content Based Image Retrieval

scholarly journals New Method of Content Based Image Retrieval based on 2-D ESPRIT Method and the Gabor Filters

2015 ◽  
Vol 15 (2) ◽  
pp. 313
Author(s):  
Chawki Youness ◽  
El Asnaoui Khalid ◽  
Ouanan Mohammed ◽  
Aksasse Brahim
Keyword(s):  
Spectral Analysis ◽  
Image Retrieval ◽  
Digital Image ◽  
Gabor Filter ◽  
Image Database ◽  
New Method ◽  
Image Texture ◽  
Content Based Image Retrieval ◽  
Image Content ◽  
Signal Parameters

We propose, in this paper, a new method for Content Based Image Retrieval (CBIR) by exploiting the digital image content. Our method is based on the representation of the digital image content by a characteristics vector of the indexed image. Indeed, we have exploited the image texture to extract its characteristics and for constructing a new descriptor vector by combining the Bidimensional High Resolution Spectral Analysis 2-D ESPRIT (Estimation of Signal Parameters via Rotationnal Invariance Techniques) method and Gabor filter. To evaluate the performance, we have tested our approach on Brodatz image database. The results show that the representation of the digital image content appears significant in research of imaging information.

Outline Shape Retrieval Using Textual Descriptors and Geometric Features

2012 ◽  
Vol 2 (4) ◽  
pp. 60-81 ◽  
Author(s):  
Saliha Aouat ◽  
Slimane Larabi
Keyword(s):  
Image Retrieval ◽  
Shape Matching ◽  
Similarity Measures ◽  
Content Based Image Retrieval ◽  
Geometric Features ◽  
Gaussian Filter ◽  
Retrieval Process ◽  
Automatic Indexing ◽  
Retrieval Processes ◽  
Textual Description

Content_based image retrieval is a promising approach because of its automatic indexing, recognition and retrieval. This paper is a contribution in the field of the content Based Image Retrieval (CBIR). Objects are represented by their outlines shapes (silhouettes) and described following the XLWDOS Textual Description (Larabi et al., 2003). Textual Descriptors are sensitive to noise. The authors have already developed an approach to smooth the outlines at different scales (Aouat & Larabi, 2010). The smoothing is performed by applying a convolution using the Gaussian Filter to process noisy shapes in order to match shapes descriptors. The authors have also applied an indexing process after silhouettes smoothing (Aouat & Larabi, 2009). The approaches (Aouat & Larabi, 2010; Aouat & Larabi, 2009) are very interesting for shape matching and indexing, but unfortunately, they are not appropriate to the recognition and retrieval processes because there is no use of similarity measures. In order to perform the retrieval process and select the best model for a query silhouette, the authors use in this paper Geometric features extracted from Textual Description of Outline Shapes.

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