A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Pedronette, Daniel Carlos Guimarães; Calumby, Rodrigo Tripodi; Torres, Ricardo da Silva

doi:10.1186/s13640-015-0081-6

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…When the item in each transaction appears once, the count is incremented by one, which belongs to the Boolean association. The Apriori algorithm should be selected first, and multi-angle analysis is performed by transforming multiple condition patterns [16].…”

Section: Research Methods a Algorithm Researchmentioning

confidence: 99%

Image Pattern Recognition Combined With Data Mining for Diagnosis and Detection of Myocardial Infarction

et al. 2020

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How to apply data analysis algorithms to China's primary hospitals is still a problem that needs to be solved. In order to effectively explore the pathogenesis of myocardial infarction disease, this study collected a large amount of real data as a basis for data analysis through data survey, improved traditional cluster analysis and data mining methods, and proposed effective data mining methods for myocardial infarction. In addition, this study analyzes data sources by implementing clustering analysis algorithms and combines data mining algorithms to provide decision support information for disease research. Finally, this study uses experimental methods of image data analysis combined with data mining methods to record the results. The research shows that the algorithm of this study has certain feasibility and can provide theoretical reference for subsequent related research.

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Section: Research Methods a Algorithm Researchmentioning

confidence: 99%

Image Pattern Recognition Combined With Data Mining for Diagnosis and Detection of Myocardial Infarction

et al. 2020

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“…Second, most hashing schemes employ only low-level visual features, and the well-known semantic gap degrades the CBIR performance [3]. To solve these problems, this paper uses the annular geometric segmentation method to extract the color moments of color images and combines fuzzy clustering and virtual correlation feedback technology to realize the self-feedback of image retrieval [10,11]. The experimental results show that the proposed method is robust to rotation, scaling, and noise and exhibits good uniqueness.…”

Section: Introductionmentioning

confidence: 99%

Self-feedback image retrieval algorithm based on annular color moments

Ding

2019

J Image Video Proc.

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Content-based image retrieval (CBIR) extracts visual content features (such as color, texture, and shape) of a sample image to retrieve another similar image. Due to the existence of the semantic gap, retrieval results are often unsatisfactory. A CBIR method based on relevance feedback (RF) can reduce the semantic gap and achieve a high-retrieval accuracy by establishing a correlation between low-level image features and high-level semantics via human-computer interaction. However, the complicated human-computer interface of RF increases the burden on users; hence, some scholars have proposed the pseudo-relevance feedback (PRF) technology. To further contribute to the research, this paper proposes a self-feedback image retrieval algorithm based on annular color moments. In this approach, hashing sequences of color moments based on annular segmentation are extracted to be used as feature vectors for initial retrieval. Based on this result, improved subtractive clustering and correlation feedback techniques are used for extended queries. Thus, a self-feedback method without user participation is realized. The experimental results show that the accuracy of image retrieval can be improved, and the proposed algorithm is robust to image rotation, scaling, and translation.

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Contextual Spaces Re‐Ranking: accelerating the Re‐sort Ranked Lists step on heterogeneous systems

Pisani

Pedronette

Torres

et al. 2016

Concurrency and Computation

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Re-ranking algorithms have been proposed to improve the effectiveness of content-based image retrieval systems by exploiting contextual information encoded in distance measures and ranked lists. In this paper, we show how we improved the efficiency of one of these algorithms, called Contextual Spaces Re-Ranking (CSRR). One of our approaches consists in parallelizing the algorithm with OpenCL to use the central and graphics processing units of an accelerated processing unit. The other is to modify the algorithm to a version that, when compared with the original CSRR, not only reduces the total running time of our implementations by a median of 1:6 but also increases the accuracy score in most of our test cases. Combining both parallelization and algorithm modification results in a median speedup of 5:4 from the original serial CSRR to the parallelized modified version. Different implementations for CSRR's Re-sort Ranked Lists step were explored as well, providing insights into graphics processing unit sorting, the performance impact of image descriptors, and the trade-offs between effectiveness and efficiency. vectors, which are later used for similarity assessment among images. A CBIR system ranks the collection images by decreasing order of similarity, and because users consider mostly top-ranked images, it is imperative that the rank be as accurate as possible.In recent years, several successful attempts to increase the effectiveness (quality of results) of CBIR systems have been performed [1][2][3][4][5][6]. In particular, re-ranking methods have been used to improve the effectiveness of CBIR systems by exploiting contextual information encoded in similarity scores and ranked lists. These methods are, on the other hand, very costly as they are based on comparing collection images multiple times. In a real-world scenario, CBIR systems require both good effectiveness and efficiency (response time), so re-ranking methods must be improved.Central processing units (CPUs) no longer have just one core, and graphics processing units (GPUs) are now being used as general purpose processors due to having evolved into massive parallel architectures capable of executing hundreds of operations per cycle [7]. These devices have been successfully used to accelerate re-ranking [7, 8] and retrieval [9] systems, obtaining good speedups.Therefore, alternatives that increase performance with parallelization seem to be a possible fit for the Contextual Spaces Re-Ranking (CSRR) algorithm [2], which we discuss in this paper. Another possible approach consists in analyzing the compromises between accuracy and performance that come out of modifying existing algorithms, because this can lead to eliminating demanding work.Our solution first exploits the use of parallelization to speed up the more costly steps of the CSRR algorithm, obtaining speedups of up to 3:3 for the Compute Distances step and 5:1 for the Re-sort Ranked Lists step on an accelerated processing unit (APU). We then propose a modification to this algorithm, which by itse...

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A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Cited by 7 publications

References 46 publications

Image Pattern Recognition Combined With Data Mining for Diagnosis and Detection of Myocardial Infarction

Image Pattern Recognition Combined With Data Mining for Diagnosis and Detection of Myocardial Infarction

Self-feedback image retrieval algorithm based on annular color moments

Contextual Spaces Re‐Ranking: accelerating the Re‐sort Ranked Lists step on heterogeneous systems

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