Segmentation of Similar Images Using Graph Matching and Community Detection

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

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Networks

10.1093/oso/9780198821939.003.0004 ◽

2018 ◽

Author(s):

Stefan Thurner ◽

Rudolf Hanel ◽

Peter Klimekl

Keyword(s):

Complex Systems ◽

Complex Networks ◽

Phase Diagrams ◽

Community Detection ◽

Network Science ◽

Small World ◽

Small World Networks ◽

Multilayer Networks ◽

Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

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Structural information and (hyper)graph matching for MRI piglet brain extraction

10th International Conference on Pattern Recognition Systems (ICPRS-2019) ◽

10.1049/cp.2019.0252 ◽

2019 ◽

Author(s):

A. Durandeau ◽

J.-B. Fasquel ◽

I. Bloch ◽

E. Mazerand ◽

P. Menei ◽

...

Keyword(s):

Graph Matching ◽

Structural Information ◽

Brain Extraction

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Optimization Algorithms for Detection of Social Interactions

Algorithms ◽

10.3390/a13060139 ◽

2020 ◽

Vol 13 (6) ◽

pp. 139 ◽

Cited By ~ 1

Author(s):

Vincenzo Cutello ◽

Georgia Fargetta ◽

Mario Pavone ◽

Rocco A. Scollo

Keyword(s):

Social Networks ◽

Local Search ◽

Social Interactions ◽

Community Detection ◽

Experimental Analysis ◽

Search Strategy ◽

Random Search ◽

Optimization Methods ◽

Research Areas ◽

Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

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