Attributed Relational SIFT-Based Regions Graph for Art Painting Retrieval

Manzo, Mario; Petrosino, Alfredo

doi:10.1007/978-3-642-41181-6_84

Cited by 11 publications

(24 citation statements)

References 54 publications

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“…In this section, we describe the proposed object recognition framework, named FastGCN + ARSRGemb, composed by different modules. In the first module, each image is encoded through an Attributed Relational SIFT-based Graph (ARSRG [16]). This structure is able to capture both local and spatial information of image.…”

Section: Framework Overviewmentioning

confidence: 99%

“…where s(G m , P i ) is a graph similarity measure between graph G m and the ith prototype. We apply this paradigm to obtain a vector where components encode the distance, obtained through an iterative and efficient graph matching algorithm [16], between the considered ARSRGs and related prototypes. Specifically, regions similarities among the ARSRGs are measured through the exploration of topological relations.…”

Section: Graph Embeddingmentioning

confidence: 99%

“…First, image representation. We encode both spatial and structural information adopting a graph based representation termed AR-SRG (Attributed Relational SIFT (Scale-Invariant Feature Transform) Regions Graph) [16]. The second concerns graph embedding.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Graph Embedding Framework for Object Recognition

Manzo

Pellino

Petrosino

et al. 2015

Lecture Notes in Computer Science

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In recent years research has been producing an important effort to encode the digital image content. Most of the adopted paradigms only focus on local features and lack in information about location and relationships between them. To fill this gap, we propose a framework built on three cornerstones. First, ARSRG (Attributed Relational SIFT (Scale-Invariant Feature Transform) regions graph), for image representation, is adopted. Second, a graph embedding model, with purpose to work in a simplified vector space, is applied. Finally, Fast Graph Convolutional Networks perform classification phase on a graph based dataset representation. The framework is evaluated on state of art object recognition datasets through a wide experimental phase and is compared with well-known competitors.

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Section: Framework Overviewmentioning

confidence: 99%

Section: Graph Embeddingmentioning

confidence: 99%

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A Novel Graph Embedding Framework for Object Recognition

Manzo

Pellino

Petrosino

et al. 2015

Lecture Notes in Computer Science

Self Cite

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“…Usually, graphs are adopted in application domains where relations among data (edges) must be highlighted [2]. Image processing [3], pattern recognition [4] and many other fields benefit from data graph representations and related manipulation algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning

Rozza¹,

Manzo²,

Petrosino³

2014

2014 22nd International Conference on Pattern Recognition

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Graph-based semi-supervised learning methods play a key role in machine learning applications, particularly when no parametric information or other prior knowledge is available. Given a graph whose nodes represent the points and the weighted edges the relations between them, the goal is to predict the values of all unlabeled nodes exploiting the information provided by both label and unlabeled nodes. In this paper, we propose a novel graph-based approach for semi-supervised binary classification. The algorithm extends the Fisher Subspace estimation approaches by adopting a kernel graph covariance measure. This similarity measure defines a relation between nodes generalizing both the shortest path and the commute time distance. This quantity is called the sum-over-paths covariance. Experiments on synthetic and real-world datasets highlight that the proposed algorithm achieves better results with respect to those obtained by state-of-the-art compet

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“…In this context, our aim is addressing two important aspects of the image classification problem. First, image representation: We faced the problem of encoding both spatial and structural information adopting a graph based representation termed ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) [1]. Differently to what happens in the dense sampling step (e.g., dense SIFT) in BoVW, our approach selects only relevant candidates through the result of image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Bag of ARSRG Words (BoAW)

Manzo

Pellino²

2019

MAKE

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In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors.

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Attributed Relational SIFT-Based Regions Graph for Art Painting Retrieval

Cited by 11 publications

References 54 publications

A Novel Graph Embedding Framework for Object Recognition

A Novel Graph Embedding Framework for Object Recognition

A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning

Bag of ARSRG Words (BoAW)

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