A Novel Graph Kernel Based SVM Algorithm for Image Semantic Retrieval

Feng, Songhe; Xu, De; Yang, Xu; Geng, Yangyang

doi:10.1007/11760023_87

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…e process of image concept retrieval (query by concept) can be summarized as follows: according to the concepts entered by the user (in this study, it refers to image aliases), such as "bus," "elephant," and "tiger," the system returns the relevant images of the concept in the image database to the user as the retrieval results [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval Technology of Economic Regulations Based on Semantic Segmentation

Guo

2022

Mathematical Problems in Engineering

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Content-based image retrieval (CBIR) is an important part of pattern recognition and artificial intelligence. It has broad application prospects in many important fields, such as digital library, medical image analysis, petroleum geological survey, and public security information retrieval. In this study, statistical modeling and discriminant learning methods are used to analyze and study some key problems in image retrieval, including image concept retrieval, image example retrieval, and relevance feedback. The main research results obtained are as follows: an image classification method based on the Gaussian mixture model (GMM) and max-min posterior pseudo-probability (MMP) discriminant learning is proposed, which is called GMM-MMP method for short; a concept retrieval method based on GMM-MMP is proposed. According to the image concept, the image is divided into two categories: the concept-related image and the concept-unrelated image. The Gaussian mixture model is used to establish the mapping from the image low-level features to the image concept, and the image is classified according to the posterior pseudo-probability classifier to realize the image concept retrieval; an example retrieval method based on GMM-MMP is proposed. According to the image similarity semantics, the image is divided into two categories: the related image and the uncorrelated image of the example image. The Gaussian mixture model is used to establish the mapping from the low-level features of the image to the image similarity semantics. The image is classified according to the posterior pseudo-probability classifier to realize the image case retrieval. Based on the above work, this study implements a content-based image retrieval system.

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Section: Introductionmentioning

confidence: 99%

Image Retrieval Technology of Economic Regulations Based on Semantic Segmentation

Guo

2022

Mathematical Problems in Engineering

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“…In this case, a pattern recognition approach suitable for a small training set is necessary. SVM has shown its capacity in pattern recognition and recently been used for content-based image retrieval [6,7,8], which is more suitable for a small training set. But all of them were single level visual models.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Modeling Approach for Medical Image Semantic Retrieval Using Hybrid Bayesian Networks

Lin

Yin

Gao

et al. 2006

Sixth International Conference on Intelligent Systems Design and Applications

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A multi-level semantic modeling method, which integrates Support Vector Machines (SVM) into hybrid Bayesian networks (HBN), is proposed in this paper. SVM discretizes the continuous variables of medical image features by classifying them into finite states as middle-level semantics. Based on the HBN, the semantic model for medical image semantic retrieval can be designed at multi-level semantics. To validate the method, a model is built to achieve automatic image annotation at the content level from a small set of astrocytona MRI (magnetic resonance imaging) samples.Multi-level annotation is a promising solution to enable medical image retrieval at different semantic levels. Experiment results show that this approach is very effective to enable multi-level interpretation of astrocytona MRI scan.It outperforms the Bayesian network-based model using k-nearest neighbor classifiers (K-NN). This study provides a novel way to bridge the gap between the high-level semantics and the low-level image features.

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