Learning an image-word embedding for image auto-annotation on the nonlinear latent space

Liu, Wei; Tang, Xiaoou

doi:10.1145/1101149.1101249

Cited by 15 publications

(9 citation statements)

References 10 publications

(15 reference statements)

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“…The Probabilistic Latent Semantic analysis (PLSA) is then used to model the resulting uniform vectored data. A variation of this approach, namely the www.ijacsa.thesai.org nonlinear latent semantic analysis was proposed in [61] to annotate images automatically. Another approach consists in formulating automatic image annotation as a classification task where unlabelled images are assigned to a set of predefined concepts such as landscape, city and sunset [62].…”

Section: A Automatic Image Annotationmentioning

confidence: 99%

A Survey on Content-based Image Retrieval

Ismail¹

2017

ijacsa

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Abstract-The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases. These databases can be counter-productive if they are not coupled with efficient Content-Based Image Retrieval (CBIR) tools. The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields. In this article, a survey on state of the art content based image retrieval including empirical and theoretical work is proposed. This work also includes publications that cover research aspects relevant to CBIR area. Namely, unsupervised and supervised learning and fusion techniques along with lowlevel image visual descriptors have been reported. Moreover, challenges and applications that emerged to support CBIR research have been discussed in this work.

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Section: A Automatic Image Annotationmentioning

confidence: 99%

A Survey on Content-based Image Retrieval

Ismail¹

2017

ijacsa

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“…Some approaches [1,8,12,13,22,20] deal directly with the problem of annotation, providing labels to either each region or the whole image. Others [4,17] treat the problem of annotation in two independent stages, first categorizing the images and then associating labels to them using the top ranked categories.…”

Section: Related Workmentioning

confidence: 99%

“…Our experiments are performed on 54, 000 images from the commonly used [1,3,4,8,12,13,17,22,20,21,14,19] Corel Stock Photo CDs, and 1, 000 publicly annotated images obtained from Yahoo! Flickr.…”

Section: Introductionmentioning

confidence: 99%

Toward bridging the annotation-retrieval gap in image search by a generative modeling approach

Datta

et al. 2006

Proceedings of the 14th ACM International Conference on Multimedia

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While automatic image annotation remains an actively pursued research topic, enhancement of image search through its use has not been extensively explored. We propose an annotation-driven image retrieval approach and argue that under a number of different scenarios, this is very effective for semantically meaningful image search. In particular, our system is demonstrated to effectively handle cases of partially tagged and completely untagged image databases, multiple keyword queries, and example based queries with or without tags, all in near-realtime. Because our approach utilizes extra knowledge from a training dataset, it outperforms state-of-the-art visual similarity based retrieval techniques. For this purpose, a novel structure-composition model constructed from Beta distributions is developed to capture the spatial relationship among segmented regions of images. This model combined with the Gaussian mixture model produces scalable categorization of generic images. The categorization results are found to surpass previously reported results in speed and accuracy. Our novel annotation framework utilizes the categorization results to select tags based on term frequency, term saliency, and a WordNet-based measure of congruity, to boost salient tags while penalizing potentially unrelated ones. A bag of words distance measure based on WordNet is used to compute semantic similarity. The effectiveness of our approach is shown through extensive experiments.

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“…In order to improve image auto-annotation, many annotation methods have been proposed integrating different domains in annotation process, such as: ontology, semantic web [1], data mining and image mining [2] [3], genetic algorithms [4], Bayesian networks [5], separators to vast margins SVM [6], probabilistic model [7], nonlinear latent space [8] and linear latent space (LSA, PLSA).…”

Section: Introductionmentioning

confidence: 99%

A comparative image auto-annotation

Bakalem

Benblidia

Ait-Aoudia

2013

IEEE International Symposium on Signal Processing and Information Technology

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The image annotation is an effective technology for improving the Web image retrieval. Many works have been proposed to increase the image auto-annotation performance. The annotation quality depends on many factors: segmentation, choice of visual features...etc.. An image contains several visuals information (color, texture, shape….) but the best choice of these parameters is a difficult problem. The main focus of this paper is two-fold. First, we compare between image annotations in latent space and textual space. Second, we survey influence of visual features choice on image annotation. For that, we developed two image auto-annotation systems (AIA-LSA, AIA-WLSA). For each one, we propose three prototypes based on different visual descriptors; such as: texture, color and fusion of both parameters.Corel data set is used to experiment our prototypes, the results show that the annotation by latent space is more efficient than the annotation by textual space and the best choice of visual features is a persistent problem in general images.

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Learning an image-word embedding for image auto-annotation on the nonlinear latent space

Cited by 15 publications

References 10 publications

A Survey on Content-based Image Retrieval

A Survey on Content-based Image Retrieval

Toward bridging the annotation-retrieval gap in image search by a generative modeling approach

A comparative image auto-annotation

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