scholarly journals Graph-Based Image Matching for Indoor Localization

2019 ◽  
Vol 1 (3) ◽  
pp. 785-804 ◽  
Author(s):  
Mario Manzo
Keyword(s):  
Data Structures ◽  
Image Matching ◽  
Indoor Localization ◽  
Graph Matching ◽  
Image Representation ◽  
Spatial Relations ◽  
Content Based Image Retrieval ◽  
Adjacency Graph ◽  
Alternative Graph ◽  
Attributed Graph

Graphs are a very useful framework for representing information. In general, these data structures are used in different application domains where data of interest are described in terms of local and spatial relations. In this context, the aim is to propose an alternative graph-based image representation. An image is encoded by a Region Adjacency Graph (RAG), based on Multicolored Neighborhood (MCN) clustering. This representation is integrated into a Content-Based Image Retrieval (CBIR) system, designed for the vision-based positioning task. The image matching phase, in the CBIR system, is managed with an approach of attributed graph matching, named the extended-VF algorithm. Evaluated in a context of indoor localization, the proposed system reports remarkable performance.

Geometric-Edge Random Graph Model for Image Representation

2012 ◽  
pp. 42-57
Author(s):  
Jiang Bo ◽  
Tang Jing ◽  
Luo Bin
Keyword(s):  
Random Graph ◽  
Image Matching ◽  
Graph Matching ◽  
Image Representation ◽  
Graph Model ◽  
Experimental Results ◽  
Structural Variations ◽  
Matching Algorithm ◽  
Random Graph Model ◽  
Dot Product

This chapter presents a random graph model for image representation. The first contribution the authors propose is a Geometric-Edge (G-E) Random Graph Model for image representation. The second contribution is that of casting image matching into G-E Random Graph matching by using the random dot product graph based matching algorithm. Experimental results show that the proposed G-E Random Graph model and matching algorithm are effective and robust to structural variations.

A Perceptual Approach for Image Representation and Retrieval

2011 ◽  
pp. 121-141
Author(s):  
Noureddine Abbadeni
Keyword(s):  
Image Retrieval ◽  
Image Representation ◽  
Rank Correlation ◽  
Human Perception ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Data Set ◽  
Psychometric Method ◽  
Results Merging ◽  
Spearman Rank Correlation Coefficient

This chapter describes an approach based on human perception to content-based image representation and retrieval. We consider textured images and propose to model the textural content of images by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast and busyness. The proposed computational measures are based on two representations: the original images representation and the autocovariance function (associated with images) representation. The correspondence of the proposed computational measures to human judgments is shown using a psychometric method based on the Spearman rank-correlation coefficient. The set of computational measures is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results show a strong correlation between the proposed computational textural measures and human perceptual judgments. The benchmarking of retrieval performance, done using the recall measure, shows interesting results. Furthermore, results merging/fusion returned by each of the two representations is shown to allow significant improvement in retrieval effectiveness.

Cerebrovascular network registration via an efficient attributed graph matching technique

2018 ◽  
Vol 46 ◽  
pp. 118-129 ◽  
Author(s):  
Sepideh Almasi ◽  
Alexandra Lauric ◽  
Adel Malek ◽  
Eric L. Miller
Keyword(s):  
Graph Matching ◽  
Attributed Graph ◽  
Matching Technique

CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION

2003 ◽  
Vol 03 (01) ◽  
pp. 119-143 ◽  
Author(s):  
ZHIYONG WANG ◽  
ZHERU CHI ◽  
DAGAN FENG ◽  
AH CHUNG TSOI
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Network Model ◽  
Neural Network Model ◽  
Relevance Feedback ◽  
Image Representation ◽  
Tree Structure ◽  
Content Based Image Retrieval ◽  
Adaptive Processing ◽  
Image Content

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.

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