CIDER: Concept-based image diversification, exploration, and retrieval

Hoque, Enamul; Hoeber, Orland; Gong, Minglun

doi:10.1016/j.ipm.2012.12.001

Cited by 15 publications

(11 citation statements)

References 39 publications

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“…With such a zoomed-in view, the overall layout probably played a minor role in the accuracy estimation. These results indicate that the task is more similar to visual search tasks like the ones described by Hoque et al [9].…”

Section: Resultssupporting

confidence: 72%

See 1 more Smart Citation

Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation

Großmann¹,

Bernard²,

Sedlmair³

et al. 2021

Preprint

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Section: Resultssupporting

confidence: 72%

“…The size of the dataset in terms of shown images and classses was either larger [9] or similar [2] to previous studies but may still be limited compared to real-world IML scenarios. Nevertheless, our results suggest that selectively looking at a subset of elements may be sufficient to estimate model accuracy.…”

Section: Study Limitations and Future Workmentioning

confidence: 95%

Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation

Großmann¹,

Bernard²,

Sedlmair³

et al. 2021

Preprint

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show abstract

“…After selecting an image, they update the transition matrix by setting the selected images as a sink point. In [16], Hoque et al expand the ambiguous query by Wikipedia and exhibit a broad range of images, which attempts to capture the various possible interpretations of the query. Ksibi et al propose a method to balance the relevance against diversity dynamically according to the ambiguity level of the given query [18].…”

Section: Retrieval Results Diversificationmentioning

confidence: 99%

“…For example, some scholars expand the query utilizing the WordNet or Wikipedia to capture the possible topics or concepts of the query tag. Hoque et al [16] expand the ambiguous query by Wikipedia and exhibit a broad range of images that represent the various possible interpretations of the query to diversify the results. In [27] and [14], the authors expand the query to the possible concept or phrase by WordNet to mine the latent topics of the query.…”

Section: Introductionmentioning

confidence: 99%

Social image retrieval based on topic diversity

Wang

Zhu

Qian

2021

Multimed Tools Appl

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Image search re-ranking is one of the most important approaches to enhance the text-based image search results. Extensive efforts have been dedicated to improve the accuracy and diversity of tag-based image retrieval. However, how to make the top-ranked results relevant and diverse is still a challenging problem. In this paper, we propose a novel method to diversify the retrieval results by latent topic analysis. We first employ NMF (Non-negative Matrix Factorization) Lee and Seung (Nature 401(6755):788–791, 1999) to estimate the initial relevance score to the query q. Then, the initial relevance score is fed into an adaptive multi-feature fusion model to learn the final relevance score. Next, the diversification process is conducted. We group all the images by semantic clustering and estimate the topic distribution of each cluster by topic analysis. The clusters are ranked based on the topic distribution vector and the final retrieval image list is obtained by a greedy selection mechanism based on the estimated relevances. Experimental results on the NUS-Wide dataset show the effectiveness of the proposed approach.

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“…Ontology are usually develop to share common understanding of information among entities or softwares where each node in the ontology is a concept containing set of attributes and relationships. The idea of using the ontologies in Image processing for content used retrieval is not new; in the last decade, researchers have proposed many efficient solutions using Ontologies for content based Image processing and retrieval such as [29][30][31][32][33][34]. The existing approaches can broadly be categorized into three types namely 1) Color based techniques 2) Shape based technique and 3) Texture based technique.…”

Section: Ontology and Image Analysismentioning

confidence: 99%

Semantic Image Retrieval: An Ontology Based Approach

Manzoor¹,

Balubaid²,

Zafar³

et al. 2015

ijarai

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Abstract-Images / Videos are major source of content on the internet and the content is increasing rapidly due to the advancement in this area. Image analysis and retrieval is one of the active research field and researchers from the last decade have proposed many efficient approaches for the same. Semantic technologies like ontology offers promising approach to image retrieval as it tries to map the low level image features to high level ontology concepts. In this paper, we have proposed Semantic Image Retrieval: An Ontology based Approach which uses domain specific ontology for image retrieval relevant to the user query. The user can give concept / keyword as text input or can input the image itself. Semantic Image Retrieval is based on hybrid approach and uses shape, color and texture based approaches for classification purpose. Mammals domain is used as a test case and its ontology is developed. The proposed system is trained on Mammals dataset and tested on large number of test cases related to this domain. Experimental results show the efficiency / accuracy of the proposed system and support the implementation of the same.

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CIDER: Concept-based image diversification, exploration, and retrieval

Cited by 15 publications

References 39 publications

Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation

Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation

Social image retrieval based on topic diversity

Semantic Image Retrieval: An Ontology Based Approach

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