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.

scholarly journals A ten-year literature review of content-based image retrieval (CBIR) studies in the tourism industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chanattra Ammatmanee ◽  
Lu Gan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Relevant Literature ◽  
Image Features ◽  
Tourism Industry ◽  
Fine Tuning ◽  
Content Based Image Retrieval ◽  
Comparative Approach ◽  
Retrieval Task ◽  
Content Type

Purpose Due to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have become a challenge for those who use and/or manage these image data across tourism settings. To overcome the image indexing task with less labour cost and improve the image retrieval task with less human errors, the content-based image retrieval (CBIR) technique has been investigated for the tourism domain particularly. This paper aims to review the relevant literature in the field to understand these previous works and identify research gaps for future directions. Design/methodology/approach A systematic and comprehensive review of CBIR studies in tourism from the year 2010 to 2019, focussing on journal articles and conference proceedings in reputable online databases, is conducted by taking a comparative approach to critically analyse and address the trends of each fundamental element in these research experiments. Findings Based on the review of the literature, the trends of CBIR studies in tourism is to improve image representation and retrieval by advancing existing feature extraction techniques, contributing novel techniques in the feature extraction process through fine-tuning fusion features and improving image query of CBIR systems. Co-authorship, tourist attraction sector and fusion image features have been in focus. Nonetheless, the number of studies in other tourism sectors and available image databases could be further explored. Originality/value The fact that no existing academic review of CBIR studies in tourism makes this paper a novel contribution.

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 Machine Learning-Based Model for Content-Based Image Retrieval

2011 ◽  
pp. 230-251 ◽  
Author(s):  
Hakim Hacid ◽  
Abdelkader Djamel Zighed
Keyword(s):  
Machine Learning ◽  
Image Retrieval ◽  
Nearest Neighbors ◽  
Multimedia Databases ◽  
Similarity Criteria ◽  
Content Based Image Retrieval ◽  
Multidimensional Space ◽  
K Nearest Neighbors ◽  
Indexing Structure ◽  
Group Data

A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the k-Nearest Neighbors (k-NN) approach to retrieve databases. Due to some disadvantages of a such approach, the use of neighborhood graphs was proposed. This approach is interesting but it has some disadvantages consisting, mainly, in its complexity. This chapter presents a step in a long process of analyzing, structuring, and retrieving multimedia databases. Indeed, we propose an effective method for locally updating neighborhood graphs which constitute our multimedia index. Then, we exploit this structure in order to make easy and effective the retrieval process using queries in an image form in one hand. In another hand, we use the indexing structure to annotate images in order to describe their semantics. The proposed approach is based on an intelligent manner for locating points in a multidimensional space. Promising results are obtained after experimentations on various databases. Future issues of the proposed approach are very relevant in this domain.

Content-Based Image Retrieval Query Paradigms

2005 ◽  
pp. 556-563 ◽  
Author(s):  
Colin C. Venters ◽  
Richard J. Hartley ◽  
William T. Hewitt
Keyword(s):  
Image Retrieval ◽  
Digital Image ◽  
Image Data ◽  
Data Capture ◽  
Content Based Image Retrieval ◽  
Spatial Relationships ◽  
Classification Schemes ◽  
Technological Advances ◽  
Retrieval Problem ◽  
And Storage

The proliferation in volume of digital image data has exacerbated the general image retrieval problem, creating a need for efficient storage and flexible retrieval of vast amounts of image data (Chang, 1989). Whilst there have been significant technological advances with image data capture and storage, developments in effective image retrieval have not kept pace. Research in image retrieval has been divided into two areas: concept-based image retrieval and content-based image retrieval. The former focuses on the use of classification schemes or indexing terms to retrieve images while the latter focuses on the visual features of the image, such as colour, shape, texture, and spatial relationships.

Content Based Image Retrieval Using Active-Nets

2011 ◽  
pp. 85-114 ◽  
Author(s):  
David García Pérez ◽  
Antonio Mosquera ◽  
Stefano Berretti ◽  
Alberto Del Bimbo
Keyword(s):  
Image Retrieval ◽  
Structural Information ◽  
Similarity Measures ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Objects ◽  
Image Retrieval System ◽  
Deformable Structure ◽  
Retrieval Problem ◽  
Active Research

Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of retrieval, but the large part of these works have focused on sub-parts of the retrieval problem, providing targeted solutions only for individual aspects (i.e., feature extraction, similarity measures, indexing, etc). In this chapter, we first shortly review some of the main practiced solutions for content-based image retrieval evidencing some of the main issues. Then, we propose an original approach for the extraction of relevant image objects and their matching for retrieval applications, and present a complete image retrieval system which uses this approach (including similarity measures and image indexing). In particular, image objects are represented by a two-dimensional deformable structure, referred to as “active net.” Active net is capable of adapting to relevant image regions according to chromatic and edge information. Extension of the active nets has been defined, which permits the nets to break themselves, thus increasing their capability to adapt to objects with complex topological structure. The resulting representation allows a joint description of color, shape, and structural information of extracted objects. A similarity measure between active nets has also been defined and used to combine the retrieval with an efficient indexing structure. The proposed system has been experimented on two large and publicly available objects databases, namely, the ETH-80 and the ALOI.

A NOVEL INDEXING AND ACCESS MECHANISM USING AFFINITY HYBRID TREE FOR CONTENT-BASED IMAGE RETRIEVAL IN MULTIMEDIA DATABASES

2007 ◽  
Vol 01 (02) ◽  
pp. 147-170 ◽  
Author(s):  
KASTURI CHATTERJEE ◽  
SHU-CHING CHEN
Keyword(s):  
Image Retrieval ◽  
Metric Spaces ◽  
Nearest Neighbor ◽  
Human Perception ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Index Structure ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Computational Overhead

An efficient access and indexing framework, called Affinity Hybrid Tree (AH-Tree), is proposed which combines feature and metric spaces in a novel way. The proposed framework helps to organize large image databases and support popular multimedia retrieval mechanisms like Content-Based Image Retrieval (CBIR). It is efficient in terms of computational overhead and fairly accurate in producing query results close to human perception. AH-Tree, by being able to introduce the high level semantic image relationship as it is in its index structure, solves the problem of translating the content-similarity measurement into feature level equivalence which is both painstaking and error-prone. Algorithms for similarity (range and k-nearest neighbor) queries are implemented and extensive experiments are performed which produces encouraging results with low I/O and distance computations and high precision of query results.

scholarly journals Interactive Content Based Image Retrieval using Multiuser Feedback

2017 ◽  
Vol 1 (4) ◽  
pp. 165
Author(s):  
M. Premkumar ◽  
R. Sowmya
Keyword(s):  
Image Retrieval ◽  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Human Perception ◽  
Content Based Image Retrieval ◽  
Rule Mining ◽  
Query Image ◽  
Feedback Techniques

Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

A Key Point Selection Shape Technique for Content Based Image Retrieval System

2016 ◽  
Vol 6 (2) ◽  
pp. 54-70 ◽  
Author(s):  
Pushpalatha Shrikant Nikkam ◽  
B. Eswara Reddy
Keyword(s):  
Image Retrieval ◽  
Template Matching ◽  
Shape Descriptor ◽  
Content Based Image Retrieval ◽  
Svm Classifier ◽  
Point Selection ◽  
Retrieval Task ◽  
Content Identification ◽  
Object Shapes ◽  
Matching Techniques

Content Based Image Retrieval (CBIR) is the process of retrieving visually similar images from huge datasets. Images are identified based on their content. Content identification using shape features is considered in this paper. Content identification using shapes is a challenging task considering multiple variations observed in images, complex backgrounds and vast categories of contents. This paper describes a shape descriptor based CBIR system. The content of an image is identified using a key point based shape descriptor. Template matching techniques are adopted to accurately describe object shapes. The object shape identified is described using histogram vectors. The use of SVM classifier for content recognition and image retrieval task is considered. Results presented prove robustness of the key point technique to accurately describe object shapes even in complex images. Performance of the proposed system is compared with existing state of art systems. Results obtained and described in the paper prove a better performance of proposed CBIR system.

scholarly journals Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4449
Author(s):  
Federico Magliani ◽  
Laura Sani ◽  
Stefano Cagnoni ◽  
Andrea Prati
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
Image Features ◽  
Retrieval Performance ◽  
Retrieval Task ◽  
K Nearest Neighbors ◽  
Diffusion Parameters ◽  
Recent Computer ◽  
Mobile Vision ◽  
And Performance

Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches.

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