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With the massive explosion of social multimedia community, social images have become very popular in our daily life. The image-associated labels are a valuable resource for automatic image annotation, but they tend to be unreliable. In this paper, we exploit the problem of image annotation from real-world community contributed images and their associated incorrect, insufficient, and personalized labels...
Automatic Image annotation is an important open problem in computer vision. In real world dataset environment, image labels are often noisy. For the task of image annotation with weakly labels, we propose SNLWL, a semantic neighborhood learning model from weakly labeled dataset. Missing labels are replenished using reweighting the error loss function. Then semantic balanced neighborhood is construct...
As a way to facilitate image categorization and retrieval, automatic image annotation has received much research attention. Traditional web image annotation methods often estimate the label relevance to image by the most common labels' frequency derived from its nearest neighbors, and neglect the relevance of the assigned label set as a whole. We propose in this paper a novel search based image annotation...
Automatic annotation can automatically annotate images with semantic labels to significantly facilitate image retrieval and organization. Traditional web image annotation methods often estimate specific label relevance to image, and neglect the relevance of the assigned label set as a whole. In this paper, A novel image annotation method by heuristic relevance learning is proposed. Label-to-image...
Automatic image annotation has emerged as an important research topic due to its potential application on both image understanding and web image search. Due to the inherent ambiguity of image-label mapping, the annotation task has become a challenge to systematically develop robust annotation models with better performance. In this paper, we present an image annotation framework based on Sparse Representation...
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