Feature-independent context estimation for automatic image annotation

Tariq, Amara; Foroosh, Hassan

doi:10.1109/cvpr.2015.7298806

Cited by 18 publications

(10 citation statements)

References 18 publications

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“…To evaluate the performance of the proposed algorithm (named PVSVDD), the results were measured by recall and precision. We compared PVSVDD with five other related algorithms, SVM, KSVM-VT[ 13 ], HBF[ 11 ], SIA[ 28 ] and FICE[ 22 ]. We compared the recall and precision of the six algorithms for 63 annotation words.…”

Section: Resultsmentioning

confidence: 99%

“…Employing jaccard similarities, Johnson et al used multiple non-parametric image metadata to identify neighbors of related images followed by a deep neural network to annotate images [ 21 ]. Tariq et al proposed a strategy that performed a tensor analysis of new images to evaluate the context, and then combined the evaluated content and the image content to label images [ 22 ]. In order to solve the problem with incomplete labels for many training image datasets, Wu et al used incomplete label data to train classifiers [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

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Visual attention mechanism and support vector machine based automatic image annotation

Hao

Wang

2018

PLoS ONE

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Automatic image annotation not only has the efficiency of text-based image retrieval but also achieves the accuracy of content-based image retrieval. Users of annotated images can locate images they want to search by providing keywords. Currently most automatic image annotation algorithms do not consider the relative importance of each region in the image, and some algorithms extract the image features as a whole. This makes it difficult for annotation words to reflect salient versus non-salient areas of the image. Users searching for images are usually only interested in the salient areas. We propose an algorithm that integrates a visual attention mechanism with image annotation. A preprocessing step divides the image into two parts, the salient regions and everything else, and the annotation step places a greater weight on the salient region. When the image is annotated, words relating to the salient region are given first. The support vector machine uses particle swarm optimization to annotate the images automatically. Experimental results show the effectiveness of the proposed algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Visual attention mechanism and support vector machine based automatic image annotation

Hao

Wang

2018

PLoS ONE

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“…In the literature this problem has been mainly approached from the tag ranking perspective. In the generative methods, which involve topic models [3,42,60,44] and mixture models [32,25,53,15,6,13], the candidate tags are naturally ranked according to their probabilities conditioned on the test image. For the non-parametric nearest neighbor based methods [37,38,35,27,22,34,61], the tags for the test image are often ranked by the votes from some training images.…”

Section: Related Workmentioning

confidence: 99%

Fast Zero-Shot Image Tagging

Zhang

Gong

Shah

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

136

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The well-known word analogy experiments show that the recent word vectors capture fine-grained linguistic regularities in words by linear vector offsets, but it is unclear how well the simple vector offsets can encode visual regularities over words. We study a particular image-word relevance relation in this paper. Our results show that the word vectors of relevant tags for a given image rank ahead of the irrelevant tags, along a principal direction in the word vector space. Inspired by this observation, we propose to solve image tagging by estimating the principal direction for an image. Particularly, we exploit linear mappings and nonlinear deep neural networks to approximate the principal direction from an input image. We arrive at a quite versatile tagging model. It runs fast given a test image, in constant time w.r.t.\ the training set size. It not only gives superior performance for the conventional tagging task on the NUS-WIDE dataset, but also outperforms competitive baselines on annotating images with previously unseen tagsComment: 9 pages (not including reference). This paper has been accepted by CVPR 201

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“…We employed similar theme-structure definition in [96] as well, but this theme structure also complements image representations in the proposed model. Themes in training data are determined by the words associated with training images.…”

Section: A Preprocessing Stagementioning

confidence: 99%