Supporting Keyword Search for Image Retrieval with Integration of Probabilistic Annotation

Zhou, Tie Hua; Wang, Ling; Ryu, Keun Ho

doi:10.3390/su7056303

Cited by 4 publications

(3 citation statements)

References 17 publications

(14 reference statements)

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“…The research concludes that learners have different learning behaviors because of different learning environments, abilities or conditions. Statistical methods commonly extract latent semantic information by using machine learning, such as support vector machine (SVM) [33], "bag-of-words" [34] and semantic orientation-pointwise mutual information [35].…”

Section: Related Workmentioning

confidence: 99%

Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

Wang

Zhou

2018

Sustainability

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Abstract:As a new education product in the information age, Massive Open Online Courses (MOOCs) command momentous public attention for their unexpected rise and flexible application. However, the striking contrast between the high rate of registration and the low rate of completion has put their development into a bottleneck. In this paper, we present a semantic analysis model (SMA) to track the emotional tendencies of learners in order to analyze the acceptance of the courses based on big data from homework completion, comments, forums and other real-time update information on the MOOC platforms. Through emotional quantification and machine learning calculations, graduation probability can be predicted for different stages of learning in real time. Especially for learners with emotional tendencies, customized instruction could be made in order to improve completion and graduation rates. Furthermore, we classified the learners into four categories according to course participation time series and emotional states. In the experiments, we made a comprehensive evaluation of the students' overall learning status by kinds of learners and emotional tendencies. Our proposed method can effectively recognize learners' emotional tendencies by semantic analysis, providing an effective solution for MOOC personalized teaching, which can help achieve education for sustainable development.

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Section: Related Workmentioning

confidence: 99%

Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

Wang

Zhou

2018

Sustainability

Self Cite

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“…In the end, MSC predicts the labels of the query image via linear combination of label vectors of labeled images weighted by the coefficients. However, MSC, like the solutions in [1,[9][10][11], is a supervised approach that asks for sufficient labeled images for sparse representation and dimensionality reduction. Nevertheless, it is very time consuming and expensive to collect sufficient labeled images.…”

Section: Related Workmentioning

confidence: 99%

“…Given that, it is necessary to take into account the multi labels' characteristics of remote sensing images and annotate a set of relevant labels to these images, instead of a single label. Recently, multi-label classification [7,8] that studies the problem where an instance annotated with a set of labels has been applied to annotate multi-label remote sensing images [1,[9][10][11] and has shown appealing performance in remote sensing applications. The multi-label classification framework has been applied to numerous real-world applications [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

et al. 2017

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Abstract:Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.

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Annotate and retrieve in vivo images using hybrid self-organizing map

Kaur,

Malhi,

Pannu

2023

Vis Comput

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Multimodal retrieval has gained much attention lately due to its effectiveness over uni-modal retrieval. For instance, visual features often under-constrain the description of an image in content-based retrieval; however, another modality, such as collateral text, can be introduced to abridge the semantic gap and make the retrieval process more efficient. This article proposes the application of cross-modal fusion and retrieval on real in vivo gastrointestinal images and linguistic cues, as the visual features alone are insufficient for image description and to assist gastroenterologists. So, a cross-modal information retrieval approach has been proposed to retrieve related images given text and vice versa while handling the heterogeneity gap issue among the modalities. The technique comprises two stages: (1) individual modality feature learning; and (2) fusion of two trained networks. In the first stage, two self-organizing maps (SOMs) are trained separately using images and texts, which are clustered in the respective SOMs based on their similarity. In the second (fusion) stage, the trained SOMs are integrated using an associative network to enable cross-modal retrieval. The underlying learning techniques of the associative network include Hebbian learning and Oja learning (Improved Hebbian learning). The introduced framework can annotate images with keywords and illustrate keywords with images, and it can also be extended to incorporate more diverse modalities. Extensive experimentation has been performed on real gastrointestinal images obtained from a known gastroenterologist that have collateral keywords with each image. The obtained results proved the efficacy of the algorithm and its significance in aiding gastroenterologists in quick and pertinent decision making.

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Supporting Keyword Search for Image Retrieval with Integration of Probabilistic Annotation

Cited by 4 publications

References 17 publications

Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

Annotate and retrieve in vivo images using hybrid self-organizing map

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