Comparison of Fourier and normal angle descriptors for the content-based image retrieval

2016 ◽  
Vol 83 (4) ◽  
Author(s):  
Pilar Hernández Mesa ◽  
Fernando Puente León
Keyword(s):  
Image Retrieval ◽  
Distance Functions ◽  
Content Based Image Retrieval ◽  
Fourier Descriptors ◽  
Shape Descriptors ◽  
Shape Information ◽  
Normal Vectors ◽  
Normal Angle

AbstractAppropriate methods are necessary to compare and search images automatically without using tags. In this work the retrieval of similar objects in images using the shape information is investigated. For this purpose, Fourier descriptors and the angles from the normal vectors along the boundaries of the objects are used as shape descriptors. Three different features are extracted from the Fourier descriptors to compare the objects. Three different distance functions are proposed to measure the similarity between the objects when the angles of the normal vectors are used as features. Finally, the appropriateness of these methods for the content-based image retrieval is compared at a commonly used database to test such cases.

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Historical Documents ◽  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 Kaous play an important role in the research of historical documents, which involve browsing and comparison of numerous samples.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. .

scholarly journals J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
João V. O. Novaes ◽  
Lúcio F. D. Santos ◽  
Luiz Olmes Carvalho ◽  
Daniel De Oliveira ◽  
Marcos V. N. Bedo ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Metric Spaces ◽  
Human Interaction ◽  
Fine Tuning ◽  
Distance Functions ◽  
Content Based Image Retrieval ◽  
Retrieval Task ◽  
K Nearest Neighbors ◽  
Retrieval Problem ◽  
Feedback Techniques

Similarity searches can be modeled by means of distances following the Metric Spaces Theory and constitute a fast and explainable query mechanism behind content-based image retrieval (CBIR) tasks. However, classical distance-based queries, e.g., Range and k-Nearest Neighbors, may be unsuitable for exploring large datasets because the retrieved elements are often similar among themselves. Although similarity searching is enriched with the imposition of rules to foster result diversification, the fine-tuning of the diversity query is still an open issue, which is is usually carried out with and a non-optimal expensive computational inspection. This paper introduces J-EDA, a practical workbench implemented in Java that supports the tuning of similarity and diversity search parameters by enabling the automatic and parallel exploration of multiple search settings regarding a user-posed content-based image retrieval task. J-EDA implements a wide variety of classical and diversity-driven search queries, as well as many CBIR settings such as feature extractors for images, distance functions, and relevance feedback techniques. Accordingly, users can define multiple query settings and inspect their performances for spotting the most suitable parameterization for a content-based image retrieval problem at hand. The workbench reports the experimental performances with several internal and external evaluation metrics such as P × R and Mean Average Precision (mAP), which are calculated towards either incremental or batch procedures performed with or without human interaction.

FUZZY ART-BASED IMAGE CLUSTERING METHOD FOR CONTENT-BASED IMAGE RETRIEVAL

2007 ◽  
Vol 06 (02) ◽  
pp. 213-233 ◽  
Author(s):  
SANG-SUNG PARK ◽  
KWANG-KYU SEO ◽  
DONG-SIK JANG
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Image Clustering ◽  
Clustering Method ◽  
Image Region ◽  
Shape Information ◽  
Fuzzy Art ◽  
The Neural Network ◽  
Joint Histogram ◽  
Noise Input

In this paper, an image clustering method that is essential for content-based image retrieval in large image databases efficiently is proposed by color, texture, and shape contents. The dominant triple HSV (Hue, Saturation, and Value), which are extracted from quantized HSV joint histogram in the image region, are used for representing color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Due to its algorithmic simplicity and the several merits that facilitate the implementation of the neural network, Fuzzy ART has been exploited for image clustering. Original Fuzzy ART suffers unnecessary increase of the number of output neurons when the noise input is presented. Therefore, the improved Fuzzy ART algorithm is proposed to resolve the problem by differently updating the committed node and uncommitted node, and checking the vigilance test again. To show the validity of the proposed algorithm, experimental results on image clustering performance and comparison with original Fuzzy ART are presented in terms of recall rates.

scholarly journals CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 294-299
Author(s):  
HARSHADA ANAND KHUTWAD ◽  
RAVINDRA JINADATTA VAIDYA
Keyword(s):  
Image Retrieval ◽  
Wavelet Decomposition ◽  
Texture Features ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Query Image ◽  
Shape Information ◽  
Color Correlogram ◽  
Similar Images ◽  
The Given

Content Based Image Retrieval is an interesting and most emerging field in the area of ‘Image Search’, finding similar images for the given query image from the image database. Current approaches include the use of color, texture and shape information. Considering these features in individual, most of the retrievals are poor in results and sometimes we are getting some non relevant images for the given query image. So, this dissertation proposes a method in which combination of color and texture features of the image is used to improve the retrieval results in terms of its accuracy. For color, color histogram based color correlogram technique and for texture wavelet decomposition technique is used. Color and texture based image

scholarly journals Image Retrieval Based on WBCH and Clustering Algorithm

2013 ◽  
Vol 5 (3) ◽  
pp. 604-613
Author(s):  
Asmita Bhaskar Shirsath ◽  
M. J. Chouhan ◽  
N. J Uke
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Similarity Measures ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Shape Information ◽  
Image Descriptors ◽  
Image Retrieval System ◽  
Retrieval Result

Research on content-based image retrieval has gained tremendous momentum during the last decade. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. In order to get faster  retrieval result from large-scale image database ,we proposed image retrieval system in which image database is first pre-processed by Wavelet Based Color Histogram (WBCH) and K-means algorithm and then using Hierarchical clustering algorithm we index the previous result and then by using similarity measures we retrieve the images from pre-processed database. Experiments show that this proposed method offers substantial increase in retrieval speed but needs to be improved on retrieval results.

Sign in / Sign up

Export Citation Format

Share Document