An Efficient and Effective Model Based on Mean Positive Examples for Social Image Annotation

Song, Haiyu; Jian, Yu; Li, Houjie; Zheng, Mingxiao; Yao, Jinxing; Lv, Hailin; Fang, A

doi:10.1109/access.2020.3039625

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Therefore, M AP L is less noisy and preferable to other perlabel metrics. Recently, more and more works use MAP as image annotation evaluation metrics [12], [39]- [41]. To more comprehensively evaluate annotation performance, we also use M AP L and M AP I as supplementary evaluation metrics for image annotation.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

Zhang

et al. 2021

IEEE Access

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Automatic image annotation is a key technology in image understanding and pattern recognition, and is becoming increasingly important in order to annotate large-scale images. In the past decade, the nearest neighbor model-based AIA methods have been proved to be the most successful in all classical models. This model has four major challenges including semantic gap, label-imbalance, wider range labels, and weak-labeling. In this paper, we propose a novel annotation model based on three-pass KNN (k-Nearest Neighbor) to address the aforementioned challenges. The key idea is to identify appropriate neighbors at each pass KNN. In the first pass KNN, we identify the several most relevant categories based on label feature rather than visual feature as traditional models. In the second pass KNN, we determine the relevant images based on multi-modal (visual and textual label) embedding features. As the test image has not been annotated with any label, we propose a pre-annotation strategory before image annotation to improve the semantic level. In the third pass KNN, we capture relevant labels from semantically and visually similar images and propagate them to the given unlabeled image. In contrast with traditional nearest neighbor based methods, our method can inherently alleviate the problems of semantic gap, label-imbalance, and wider range labels. In addition, to alleviate the issue of weak-labeling, we propose label refinement for training images. Extensive experiments on three classical benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art in terms of per-label and per-image metrics.INDEX TERMS Automatic image annotation, semantic gap, nearest neighbor, weak-labeling.

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Section: B Evaluation Metricsmentioning

confidence: 99%

Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

Zhang

et al. 2021

IEEE Access

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Visual element recognition based on profile coefficient and image processing technology

Luo

2023

Paladyn, Journal of Behavioral Robotics

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A method based on the contour coefficient and image processing technology is proposed to better identify the visual elements. This article takes the contour of the image as the recognition feature, summarizes the methods of target contour feature extraction, contour shape representation, and similarity representation, and studies the processing methods of contour edge preserving and denoising, contour feature simplification and description methods, and contour matching methods. This problem can usually be solved by filling out a form. Generally, a simple iterative equation is given to express the direct relationship between the current table and the calculated table values. The dynamic programming algorithm of inner distance shape context, multi-scale convexity convexity, and triangle area representation finds the best sequence correspondence.

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The Image Annotation Refinement in Embedding Feature Space based on Mutual Information

Song

Zhang

et al. 2022

International Journal of Circuits, Systems and Signal Processing

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The ever-increasing size of images has made automatic image annotation one of the most important tasks in the fields of machine learning and computer vision. Despite continuous efforts in inventing new annotation algorithms and new models, results of the state-of-the-art image annotation methods are often unsatisfactory. In this paper, to further improve annotation refinement performance, a novel approach based on weighted mutual information to automatically refine the original annotations of images is proposed. Unlike the traditional refinement model using only visual feature, the proposed model use semantic embedding to properly map labels and visual features to a meaningful semantic space. To accurately measure the relevance between the particular image and its original annotations, the proposed model utilize all available information including image-to-image, label-to-label and image-to-label. Experimental results conducted on three typical datasets show not only the validity of the refinement, but also the superiority of the proposed algorithm over existing ones. The improvement largely benefits from our proposed mutual information method and utilizing all available information.

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An Efficient and Effective Model Based on Mean Positive Examples for Social Image Annotation

Cited by 3 publications

References 38 publications

Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

Visual element recognition based on profile coefficient and image processing technology

The Image Annotation Refinement in Embedding Feature Space based on Mutual Information

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