Fuzzy VA-Files for multi-label image annotation based on visual content of regions

Rejeb, Imen Ben; Ouni, Sonia; Barhoumi, Walid; Zagrouba, Ezzeddine

doi:10.1007/s11760-017-1233-1

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Extensive methods of image classification tasks in the literature focus on exploring the correlations among labels. Probabilistic graph models are widely utilized to formulate the coexistence of labels in early research [28].…”

Section: Label Correlationsmentioning

confidence: 99%

Semantic Understandings for Aerial Images via Multigrained Feature Grouping

Lin

Chen

2022

Scientific Programming

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Aerial images play a key role in remote sensing as they can provide high-quality surface object information for continuous communication services. With advances in UAV-aided data collection technologies, the volume of aerial images has been greatly promoted. To this end, semantic understandings for these images can significantly improve the quality of service for smart devices. Recently, the multilabel aerial image classification (MAIC) task has been widely researched in academics and applied in industries. However, existing MAIC methods suffer from suboptimal performance as objects are located in different sizes and scales. To address these issues, we propose a novel multigrained semantic grouping model for aerial image learning, named MSGM. First, image features presented by the backbone are sent to spatial pyramid convolutional layers which extract the instances in a parallel manner. Then, three grouping mechanisms are designed to integrate the instances from the pyramid framework. In addition, MSGM builds a concept graph to represent the label relationship. MSGM resorts to the graph convolutional network to learn the concept graph directly. We extensively evaluate MSGM on two benchmark aerial image datasets, the commonly used UCM dataset, and the high-resolution DFC15 dataset. Quantitative and qualitative results support the effectiveness of the proposed MSGM.

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Section: Label Correlationsmentioning

confidence: 99%

Semantic Understandings for Aerial Images via Multigrained Feature Grouping

Lin

Chen

2022

Scientific Programming

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“…Because the traits and indicators of various regions are not taken into consideration, such holistic methods cannot discover all important concepts that may be represented within the image. Other region-based methods [13][14][15][16][17][18][19], on the other hand, have emphasized establishing a one-to-one correlation between concept and region (i.e., each region represents one concept). Such a region-level semantic is more beneficial for figuring out the connections between semantic ideas and visual objects in images.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation

et al. 2021

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Automatic image annotation is an active field of research in which a set of annotations are automatically assigned to images based on their content. In literature, some works opted for handcrafted features and manual approaches of linking concepts to images, whereas some others involved convolutional neural networks (CNNs) as black boxes to solve the problem without external interference. In this work, we introduce a hybrid approach that combines the advantages of both CNN and the conventional concept-to-image assignment approaches. J-image segmentation (JSEG) is firstly used to segment the image into a set of homogeneous regions, then a CNN is employed to produce a rich feature descriptor per area, and then, vector of locally aggregated descriptors (VLAD) is applied to the extracted features to generate compact and unified descriptors. Thereafter, the not too deep clustering (N2D clustering) algorithm is performed to define local manifolds constituting the feature space, and finally, the semantic relatedness is calculated for both image–concept and concept–concept using KNN regression to better grasp the meaning of concepts and how they relate. Through a comprehensive experimental evaluation, our method has indicated a superiority over a wide range of recent related works by yielding F1 scores of 58.89% and 80.24% with the datasets Corel 5k and MSRC v2, respectively. Additionally, it demonstrated a relatively high capacity of learning more concepts with higher accuracy, which results in N+ of 212 and 22 with the datasets Corel 5k and MSRC v2, respectively.

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