Distance Functions Association for Content-Based Image Retrieval using Multiple Comparison Criteria

2006 ◽  
Author(s):  
I.R.V. Pola ◽  
A.J.M. Traina ◽  
C. Traina
Keyword(s):  
Image Retrieval ◽  
Distance Functions ◽  
Multiple Comparison ◽  
Content Based Image Retrieval ◽  
Comparison Criteria

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Historical Documents ◽  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 Kaous play an important role in the research of historical documents, which involve browsing and comparison of numerous samples.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

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.

FIXED CONTENT BASED IMAGE RETRIEVAL TECHNIQUE

2017 ◽  
Vol 5 (3) ◽  
pp. 54
Author(s):  
MOHAMMED ILIAS SHAIK ◽  
CHAUHAN DINESH ◽  
ESAPALLI SRINIVAS ◽  
PADIGE VINEETH ◽  
◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Technique

Content Based Image Retrieval and Clustering: A Brief Survey

2009 ◽  
Vol 2 (3) ◽  
pp. 187-199 ◽  
Author(s):  
Huiyu Zhou ◽  
Abdul Sadka ◽  
Mohammad Swash ◽  
Jawid Azizi ◽  
Abubakar Umar
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval
Sign in / Sign up

Export Citation Format

Share Document