Multimodal Markov Random Field for Image Reranking Based on Relevance Feedback

This paper introduces a multimodal approach for reranking of image retrieval results based on relevance feedback. We consider the problem of reordering the ranked list of images returned by an image retrieval system, in such a way that relevant images to a query are moved to the first positions of the list. We propose a Markov random field (MRF) model that aims at classifying the images in the initial retrieval-result list as relevant or irrelevant; the output of the MRF is used to generate a new list of ranked images. The MRF takes into account (1) the rank information provided by the initial retrieval system, (2) similarities among images in the list, and (3) relevance feedback information. Hence, the problem of image reranking is reduced to that of minimizing an energy function that represents a trade-off between image relevance and interimage similarity. The proposed MRF is a multimodal as it can take advantage of both visual and textual information by which images are described with. We report experimental results in the IAPR TC12 collection using visual and textual features to represent images. Experimental results show that our method is able to improve the ranking provided by the base retrieval system. Also, the multimodal MRF outperforms unimodal (i.e., either text-based or image-based) MRFs that we have developed in previous work. Furthermore, the proposed MRF outperforms baseline multimodal methods that combine information from unimodal MRFs.

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Document ranking refinement using a Markov random field model

Natural Language Engineering ◽

10.1017/s1351324912000010 ◽

2012 ◽

Vol 18 (2) ◽

pp. 155-185 ◽

Cited By ~ 2

Author(s):

ESAÚ VILLATORO ◽

ANTONIO JUÁREZ ◽

MANUEL MONTES ◽

LUIS VILLASEÑOR ◽

L. ENRIQUE SUCAR

Keyword(s):

Random Field ◽

Markov Random Field ◽

Relevance Feedback ◽

Document Retrieval ◽

Document Similarity ◽

Feedback Information ◽

Markov Random ◽

Novel Method ◽

Cross Language ◽

Recent Evaluation

AbstractThis paper introduces a novel ranking refinement approach based on relevance feedback for the task of document retrieval. We focus on the problem of ranking refinement since recent evaluation results from Information Retrieval (IR) systems indicate that current methods are effective retrieving most of the relevant documents for different sets of queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results, we propose a novel method to re-rank the list of documents returned by an IR system. The proposed method is based on a Markov Random Field (MRF) model that classifies the retrieved documents as relevant or irrelevant. The proposed MRF combines: (i) information provided by the base IR system, (ii) similarities among documents in the retrieved list, and (iii) relevance feedback information. Thus, the problem of ranking refinement is reduced to that of minimising an energy function that represents a trade-off between document relevance and inter-document similarity. Experiments were conducted using resources from four different tasks of the Cross Language Evaluation Forum (CLEF) forum as well as from one task of the Text Retrieval Conference (TREC) forum. The obtained results show the feasibility of the method for re-ranking documents in IR and also depict an improvement in mean average precision compared to a state of the art retrieval machine.

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A New Method for Medical Image Retrieval Based on Markov Random Field

Communications in Computer and Information Science - Data Science ◽

10.1007/978-981-10-6385-5_38 ◽

2017 ◽

pp. 447-461

Author(s):

Tiaodi Wang ◽

Haiwei Pan ◽

Xiaoqin Xie ◽

Zhiqiang Zhang ◽

Xiaoning Feng

Keyword(s):

Image Retrieval ◽

Random Field ◽

Markov Random Field ◽

Medical Image ◽

New Method ◽

Medical Image Retrieval ◽

Markov Random

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Color-Texture-Based Image Retrieval System Using Gaussian Markov Random Field Model

Mathematical Problems in Engineering ◽

10.1155/2009/410243 ◽

2009 ◽

Vol 2009 ◽

pp. 1-17 ◽

Cited By ~ 5

Author(s):

Meng-Hsiun Tsai ◽

Yung-Kuan Chan ◽

Jiun-Shiang Wang ◽

Shu-Wei Guo ◽

Jiunn-Lin Wu

Keyword(s):

Image Retrieval ◽

Random Field ◽

Markov Random Field ◽

Field Model ◽

Query Image ◽

Retrieval Method ◽

Random Field Model ◽

Markov Random Field Model ◽

Gaussian Markov Random Field ◽

Markov Random

The techniques of -means algorithm and Gaussian Markov random field model are integrated to provide a Gaussian Markov random field model (GMRFM) feature which can describe the texture information of different pixel colors in an image. Based on this feature, an image retrieval method is also provided to seek the database images most similar to a given query image. In this paper, a genetic-based parameter detector is presented to decide the fittest parameters used by the proposed image retrieval method, as well. The experimental results manifested that the image retrieval method is insensitive to the rotation, translation, distortion, noise, scale, hue, light, and contrast variations, especially distortion, hue, and contrast variations.

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An Efficient and Effective Image Retrieval System on the basis of (Feature, Matching Measure and sub-space) Selection

Journal of information and organizational sciences ◽

10.31341/jios.42.2.4 ◽

2018 ◽

Vol 42 (2) ◽

pp. 231-240

Author(s):

Mawloud Mosbah

Keyword(s):

Image Retrieval ◽

Relevance Feedback ◽

Retrieval System ◽

Feature Matching ◽

Research Field ◽

Content Based Image Retrieval ◽

Review Of Literature ◽

Or Efficiency ◽

Feedback Information ◽

Image Retrieval System

Since its appearance as a research field, Content-based Image Retrieval (CBIR) system has increasingly received an important attention. Review of literature reveals that the efforts put, up to now, in the field address either effectiveness or efficiency. In this paper, we address both accuracy and efficiencythrough introducing an efficient and an effective image retrieval approach based on feature, matching measure and sub-spaceselection. The selection relies on relevance feedback information injected by the user. The approach is tested on Corel-1Kimages database. The obtained results are very promising.

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DNA Microarray Image Segmentation Using Markov Random Field Algorithm

Journal of Physics Conference Series ◽

10.1088/1742-6596/2071/1/012032 ◽

2021 ◽

Vol 2071 (1) ◽

pp. 012032

Author(s):

K A Mat Said ◽

A B Jambek

Keyword(s):

Image Analysis ◽

Image Segmentation ◽

Random Field ◽

Dna Microarray ◽

Markov Random Field ◽

Deoxyribonucleic Acid ◽

Experimental Results ◽

Microarray Image ◽

Markov Random ◽

Stage Process

Abstract A deoxyribonucleic acid (DNA) microarray image requires a three-stage process to enhance and preserve the image’s important information. These are gridding, segmentation, and intensity extraction. Of these three processes, segmentation is considered the most difficult, as its function is to differentiate between features in the foreground and background. The elements in the foreground form the object or the vital information of the image, while the background features less critical information for DNA microarray image analysis. This paper presents a study that utilises the Markov random field (MRF) segmentation algorithm on a DNA microarray image. The MRF algorithm evaluates the current pixel depends on its neighbouring pixels. The experimental results show that the MRF algorithm works effectively in the segmentation process for a DNA microarray image.

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A Two-Stage of Relevance Feedback for Content-Based Image Retrieval

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.1627 ◽

2011 ◽

Vol 467-469 ◽

pp. 1627-1632

Author(s):

Xue Feng Wang ◽

Xing Su Chen

Keyword(s):

Logistic Regression ◽

Image Retrieval ◽

Relevance Feedback ◽

Retrieval System ◽

Image Database ◽

Experimental Results ◽

Content Based Image Retrieval ◽

Target Image ◽

Two Stage ◽

Predictive Probabilities

In this paper, an effective relevance feedback (RF) approach is proposed in content-based image retrieval (CBIR). In the first stage, according to the user’s marked images, we get theirs predictive probabilities based-on Bayesian methodology which yields the posteriori of the images in the database; second via justify the weight of elements in each feature extracted of images, we refine features by logistic regression with positive features which get from the first stage. Then we rank the images according to the probability of the images in the database. The retrieval system is repeating until the user is satisfied with the feedback results or the target image has been found. Experimental results are shown to evaluate the method on a large image database in terms of precision and recall.

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Combining Convolutional Neural Network and Markov Random Field for Semantic Image Retrieval

Advances in Multimedia ◽

10.1155/2018/6153607 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11

Author(s):

Haijiao Xu ◽

Changqin Huang ◽

Xiaodi Huang ◽

Chunyan Xu ◽

Muxiong Huang

Keyword(s):

Neural Network ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Random Field ◽

Markov Random Field ◽

Concept Detection ◽

Semantic Image Retrieval ◽

Semantic Correlation ◽

Markov Random ◽

Fully Connected

With the rapidly growing number of images over the Internet, efficient scalable semantic image retrieval becomes increasingly important. This paper presents a novel approach for semantic image retrieval by combining Convolutional Neural Network (CNN) and Markov Random Field (MRF). As a key step, image concept detection, that is, automatically recognizing multiple semantic concepts in an unlabeled image, plays an important role in semantic image retrieval. Unlike previous work that uses single-concept classifiers one by one, we detect semantic multiconcept by using a multiconcept scene classifier. In other words, our approach takes multiple concepts as a holistic scene for multiconcept scene learning. Specifically, we first train a CNN as a concept classifier, which further includes two types of classifiers: a single-concept fully connected classifier that is best suited to single-concept detection and a multiconcept scene fully connected classifier that is good for holistic scene detection. Then we propose an MRF-based late fusion approach that is able to effectively learn the semantic correlation between the single-concept classifier and multiconcept scene classifier. Finally, the semantic correlation among the subconcepts of images is cought to further improve detection precision. In order to investigate the feasibility and effectiveness of our proposed approach, we conduct comprehensive experiments on two publicly available image databases. The results show that our proposed approach outperforms several state-of-the-art approaches.

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