Convolutional neural networks for relevance feedback in content based image retrieval

Putzu, Lorenzo; Piras, Luca; Giacinto, Giorgio

doi:10.1007/s11042-020-09292-9

Cited by 34 publications

(17 citation statements)

References 46 publications

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“…No medical knowledge was used in this process; hence, there exists the domain gap if we want to apply the systems to medical domain. This loss of information can be reduced by incorporating prior knowledge and other sources of knowledge [ 78 ]. Table 5 lists the overview of datasets used in the image retrieval articles included in this review.…”

Section: Resultsmentioning

confidence: 99%

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

et al. 2022

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Background. There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledge representations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodes and edges to represent semantically connected knowledge entities, which several works have adopted into different medical imaging applications. Methods. We systematically searched over five databases to find relevant articles that applied knowledge graphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematic analysis. Results. We looked at four applications in medical imaging analysis, including disease classification, disease localization and segmentation, report generation, and image retrieval. We also identified limitations of current work, such as the limited amount of available annotated data and weak generalizability to other tasks. We further identified the potential future directions according to the identified limitations, including employing semisupervised frameworks to alleviate the need for annotated data and exploring task-agnostic models to provide better generalizability. Conclusions. We hope that our article will provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medical imaging to encourage future research.

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Section: Resultsmentioning

confidence: 99%

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

et al. 2022

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“…We extracted the features from the last fully connected layer for a total of 23 features. The CNNs are known to have a sufficient representational power and generalisation ability to perform different visual recognition tasks [ 42 ]. Nevertheless, we fine-tuned the above CNNs on both data sets before the feature extraction in order to produce a fairer comparison to the standard machine learning classifiers trained with handcrafted features.…”

Section: Materials and Methodsmentioning

confidence: 99%

On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

Loddo

Ruberto

2021

J. Imaging

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Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework.

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“…By comparing these baseline methods and our method with them, we confirm that our re-ranking method can improve the initial retrieval performance. Also, basic re-ranking methods [12], [13], [31], [40], [41], [42], [43], [44] were utilized as comparative methods, respectively. By using these various types of re-ranking methods, we confirm that our re-ranking ( f E and f E n ) via the encoders M L (•) and M V (•) following each initial cross-modal retrieval method.…”

Section: Microsoft Common Objects In Context (Mscoco) [33]mentioning

confidence: 99%

Recallable Question Answering-Based Re-Ranking Considering Semantic Region for Cross-Modal Retrieval

Yanagi

Togo

Ogawa

et al. 2023

IEEE Open J. Signal Process.

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Question answering (QA)-based re-ranking methods for cross-modal retrieval have been recently proposed to further narrow down similar candidate images. The conventional QA-based re-ranking methods provide questions to users by analyzing candidate images, and the initial retrieval results are re-ranked based on the user's feedback. Contrary to these developments, only focusing on performance improvement makes it difficult to efficiently elicit the user's retrieval intention. To realize more useful QA-based re-ranking, considering the user interaction for eliciting the user's retrieval intention is required. In this paper, we propose a QA-based re-ranking method with considering two important factors for eliciting the user's retrieval intention: query-image relevance and recallability. Considering the query-image relevance enables to only focus on the candidate images related to the provided query text, while, focusing on the recallability enables users to easily answer the provided question. With these procedures, our method can efficiently and effectively elicit the user's retrieval intention. Experimental results using Microsoft Common Objects in Context and computationally constructed dataset including similar candidate images show that our method can improve the performance of the cross-modal retrieval methods and the QA-based re-ranking methods.

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Convolutional neural networks for relevance feedback in content based image retrieval

Cited by 34 publications

References 46 publications

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review

On the Efficacy of Handcrafted and Deep Features for Seed Image Classification

Recallable Question Answering-Based Re-Ranking Considering Semantic Region for Cross-Modal Retrieval

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