Image Retrieval Based on a Multi-Integration Features Model

Chu, Kai; Liu, Guanghai

doi:10.1155/2020/1461459

Cited by 27 publications

(8 citation statements)

References 41 publications

(68 reference statements)

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“…To assess the performance and effectiveness of the image retrieval process, the value of precision and recall is calculated as follows [10], [30]:…”

Section: Performance Evaluationmentioning

confidence: 99%

Image retrieval based on swarm intelligence

Khaleel

khaled

2021

IJECE

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To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

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“…To assess the performance and effectiveness of the image retrieval process, the value of precision and recall is calculated as follows [10], [30]:…”

Section: Performance Evaluationmentioning

confidence: 99%

Image retrieval based on swarm intelligence

Khaleel

khaled

2021

IJECE

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“…4) EDBTC [58] and MIFM [59] methods, which depend on extracting both texture and color features. Our proposed method gave better performance than previous methods regarding Precision, Recall, mAP, and ARR for all databases, because it works on extracting the color changes of minimum and maximum intensities of images blocks, in addition to the texture features of the images.…”

Section: A First Experiment: Image Retrieval Using Fdlnpmentioning

confidence: 99%

Full Direction Local Neighbors Pattern (FDLNP)

Alrahhal¹,

K.P²

2021

IJACSA

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In this paper, we proposed the Full Direction Local Neighbor Pattern (FDLNP) algorithm, which is a novel method for Content-Based Image Retrieval. FDLNP consists of many steps, starting from generating Max and Min Quantizers followed by building two matrix types (the Eight Neighbors Euclidean Decimal Coding matrix, and Full Direction Matrixes). After that, we extracted Gray-Level Co-occurrence Matrix (GLCM) from those matrixes to derive the important features from each GLCM matrixes and finishing with merging the output of previous steps with Local Neighbor Patterns (LNP) histogram. For decreasing the feature vector length, we proposed five extension methods from FDLNP by choosing the specific direction matrixes. Our results demonstrate the effectiveness of our proposed algorithm on color and texture databases, comparing with recent works, with regard to the Precision, Recall, mean Average Precision (mAP), and Average Retrieval Rate (ARR). For enhancing the image retrieval accuracy, we proposed a novel framework that combined the image retrieval system with clustering and classification algorithms. Moreover, we proposed a distributed model that used our FDLNP method with Hadoop to get the ability to process a huge number of images in a reasonable time.

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“…Feature addition stands out for its high accuracy [24]. Chu et al [25] proposed a multi-modal feature fusion method based on weight distribution, and demonstrated that the method significantly outperforms single feature fusion. In addition to the extracted features like tongue color, moss thickness, and face color, PSQI score (rang: 0-21) and sleep structure score (range: 0-100) were adopted to indicate sleep quality.…”

Section: Hemies Design and Implementationmentioning

confidence: 99%

A Home Efficacy Multi-Modal Intelligent Evaluation System for Wearable Treatment Equipment of Insomnia Through Integration Between Traditional Chinese Medicine and Modern Medicine

Zhang¹,

Ogihara²,

Zhou³

et al. 2021

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Wearable treatment equipment has become a hot topic among traditional Chinese medicine (TCM) researchers. The wearable instrument for transcutaneous electrical stimulation therapy (TEST) developed by our research team has unique advantages in treating insomnia induced by the heart disorder due to phlegm and fire (HDPF). However, the treatment efficacy of the instrument has not been verified by multi-modal data, which limits its application at home. In fact, there is no intelligent efficacy evaluation system for the home treatment of insomnia with the instrument. To make up the gap, this paper attempts to build a home efficacy multi-modal intelligent evaluation system (HEMIES) for this wearable TEST instrument, drawing on the latest technologies of medicine and informatics. Taking HDPF-induced insomnia as an example, the authors firstly set up an index system of the HEMIES for HDPF-induced insomnia, and constructed a conceptual model of the HEMIES. Next, the syndrome images of TCM were fused with the text data of modern medicine, and used to evaluate the treatment efficacy and complete the HEMIES. Finally, 33 patients with HDPF-induced insomnia were recruited to treat the disease with the wearable TEST instrument at home. The observed results show that the prediction accuracy rate of the HEMIES was as high as 90.63%, indicating that the system boasts a good prediction ability and a high value for home application. Our HEMIES realizes the objective evaluation of the home treatment efficacy of the wearable TEST instrument in the absence of TCM practitioners, and provides a reference for the efficacy research of other TCM treatment equipment in home treatment.

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Image Retrieval Based on a Multi-Integration Features Model

Cited by 27 publications

References 41 publications

Image retrieval based on swarm intelligence

Image retrieval based on swarm intelligence

Full Direction Local Neighbors Pattern (FDLNP)

A Home Efficacy Multi-Modal Intelligent Evaluation System for Wearable Treatment Equipment of Insomnia Through Integration Between Traditional Chinese Medicine and Modern Medicine

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