Content-based Image Retrieval Based On Visual Attention And The Conditional Probability

Liu, Guanghai

doi:10.2991/cmfe-15.2015.199

Cited by 10 publications

(2 citation statements)

References 20 publications

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“…In CBIR systems, the capability of a particular system can be concluded with respect to many evaluation parameters [39]- [40]. Precision and Recall are the most well-known evaluation metrics.…”

Section: A Methodsmentioning

confidence: 99%

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Shikha¹,

Gitanjali²,

Kumar³

2020

IJIMAI

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The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system.

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Section: A Methodsmentioning

confidence: 99%

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Shikha¹,

Gitanjali²,

Kumar³

2020

IJIMAI

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“…Discrete wavelet transform, LBP, and grey level co-occurrence matrixes can be used to exploit multiresolution analysis and to enhance image directional information [26][27][28][29][30]. Simulation of human perception and visual attention have been adopted in some multistage image retrieval frameworks [31][32][33][34][35][36][37][38][39][40][41]. e manifold ranking method has been used to match similar feature vectors [42,43].…”

Section: Related Workmentioning

confidence: 99%

Image Retrieval Using the Intensity Variation Descriptor

Zhao

Liu

2020

Mathematical Problems in Engineering

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Variations between image pixel characteristics contain a wealth of information. Extraction of such cues can be used to describe image content. In this paper, we propose a novel descriptor, called the intensity variation descriptor (IVD), to represent variations in colour, edges, and intensity and apply it to image retrieval. The highlights of the proposed method are as follows. (1) The IVD combines the advantages of the HSV and RGB colour spaces. (2) It can simulate the lateral inhibition mechanism and orientation-selective mechanism to determine an optimal direction and spatial layout. (3) An extended weighted L1 distance metric is proposed to calculate the similarity of images. It does not require complex operations such as square or square root and leads to good performance. Comparative experiments on two Corel datasets containing 15,000 images show that the proposed method performs better than the SoC-GMM, CPV-THF, and STH methods and provides good matching of texture, colour, and shape.

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Deep Convolutional Network Based Saliency Prediction for Retrieval of Natural Images

Nandagopalan

Kumar²

2018

Advances in Intelligent Systems and Computing

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Content-based Image Retrieval Based On Visual Attention And The Conditional Probability

Cited by 10 publications

References 20 publications

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Image Retrieval Using the Intensity Variation Descriptor

Deep Convolutional Network Based Saliency Prediction for Retrieval of Natural Images

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