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Automatic image annotation

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Output of DenseCap "dense captioning" software, analysing a photograph of a man riding an elephant

Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database.

This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations. Subsequently, techniques were developed using machine translation to to attempt to translate the textual vocabulary into the 'visual vocabulary,' represented by clustered regions known as blobs. Subsequent work has included classification approaches, relevance models, and other related methods.

The advantages of automatic image annotation versus content-based image retrieval (CBIR) are that queries can be more naturally specified by the user.[1] At present, Content-Based Image Retrieval (CBIR) generally requires users to search by image concepts such as color and texture or by finding example queries. However, certain image features in example images may override the concept that the user is truly focusing on. Traditional methods of image retrieval, such as those used by libraries, have relied on manually annotated images, which is expensive and time-consuming, especially given the large and constantly growing image databases in existence.

See also

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References

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  1. ^ "Archived copy" (PDF). i.yz.yamagata-u.ac.jp. Archived from the original (PDF) on 8 August 2014. Retrieved 13 January 2022.{{cite web}}: CS1 maint: archived copy as title (link)

Further reading

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  • Word co-occurrence model
Y Mori; H Takahashi & R Oka (1999). "Image-to-word transformation based on dividing and vector quantizing images with words.". Proceedings of the International Workshop on Multimedia Intelligent Storage and Retrieval Management. CiteSeerX 10.1.1.31.1704.
  • Annotation as machine translation
P Duygulu; K Barnard; N de Fretias & D Forsyth (2002). "Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary". Proceedings of the European Conference on Computer Vision. pp. 97–112. Archived from the original on 2005-03-05.
  • Statistical models
J Li & J Z Wang (2006). "Real-time Computerized Annotation of Pictures". Proc. ACM Multimedia. pp. 911–920.
J Z Wang & J Li (2002). "Learning-Based Linguistic Indexing of Pictures with 2-D MHMMs". Proc. ACM Multimedia. pp. 436–445.
  • Automatic linguistic indexing of pictures
J Li & J Z Wang (2008). "Real-time Computerized Annotation of Pictures". IEEE Transactions on Pattern Analysis and Machine Intelligence.
J Li & J Z Wang (2003). "Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach". IEEE Transactions on Pattern Analysis and Machine Intelligence. pp. 1075–1088.
  • Hierarchical Aspect Cluster Model
K Barnard; D A Forsyth (2001). "Learning the Semantics of Words and Pictures". Proceedings of International Conference on Computer Vision. pp. 408–415. Archived from the original on 2007-09-28.
  • Latent Dirichlet Allocation model
D Blei; A Ng & M Jordan (2003). "Latent Dirichlet allocation" (PDF). Journal of Machine Learning Research. pp. 3:993–1022. Archived from the original (PDF) on March 16, 2005.
G Carneiro; A B Chan; P Moreno & N Vasconcelos (2006). "Supervised Learning of Semantic Classes for Image Annotation and Retrieval" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. pp. 394–410.
  • Texture similarity
R W Picard & T P Minka (1995). "Vision Texture for Annotation". Multimedia Systems.
  • Support Vector Machines
C Cusano; G Ciocca & R Scettini (2004). Santini, Simone & Schettini, Raimondo (eds.). "Image Annotation Using SVM". Internet Imaging V. 5304: 330–338. Bibcode:2003SPIE.5304..330C. doi:10.1117/12.526746. S2CID 16246057.
  • Ensemble of Decision Trees and Random Subwindows
R Maree; P Geurts; J Piater & L Wehenkel (2005). "Random Subwindows for Robust Image Classification". Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. pp. 1:34–30.
  • Maximum Entropy
J Jeon; R Manmatha (2004). "Using Maximum Entropy for Automatic Image Annotation" (PDF). Int'l Conf on Image and Video Retrieval (CIVR 2004). pp. 24–32.
  • Relevance models
J Jeon; V Lavrenko & R Manmatha (2003). "Automatic image annotation and retrieval using cross-media relevance models" (PDF). Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 119–126.
  • Relevance models using continuous probability density functions
V Lavrenko; R Manmatha & J Jeon (2003). "A model for learning the semantics of pictures" (PDF). Proceedings of the 16th Conference on Advances in Neural Information Processing Systems NIPS.
  • Coherent Language Model
R Jin; J Y Chai; L Si (2004). "Effective Automatic Image Annotation via A Coherent Language Model and Active Learning" (PDF). Proceedings of MM'04.
  • Inference networks
D Metzler & R Manmatha (2004). "An inference network approach to image retrieval" (PDF). Proceedings of the International Conference on Image and Video Retrieval. pp. 42–50.
  • Multiple Bernoulli distribution
S Feng; R Manmatha & V Lavrenko (2004). "Multiple Bernoulli relevance models for image and video annotation" (PDF). IEEE Conference on Computer Vision and Pattern Recognition. pp. 1002–1009.
  • Multiple design alternatives
J Y Pan; H-J Yang; P Duygulu; C Faloutsos (2004). "Automatic Image Captioning" (PDF). Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME'04). Archived from the original (PDF) on 2004-12-09.
  • Image captioning
Quan Hoang Lam; Quang Duy Le; Kiet Van Nguyen; Ngan Luu-Thuy Nguyen (2020). "UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning". Proceedings of the 2020 International Conference on Computational Collective Intelligence (ICCCI 2020). arXiv:2002.00175. doi:10.1007/978-3-030-63007-2_57.
  • Natural scene annotation
J Fan; Y Gao; H Luo; G Xu (2004). "Automatic Image Annotation by Using Concept-Sensitive Salient Objects for Image Content Representation". Proceedings of the 27th annual international conference on Research and development in information retrieval. pp. 361–368.
  • Relevant low-level global filters
A Oliva & A Torralba (2001). "Modeling the shape of the scene: a holistic representation of the spatial envelope" (PDF). International Journal of Computer Vision. pp. 42:145–175.
  • Global image features and nonparametric density estimation
A Yavlinsky, E Schofield & S Rüger (2005). "Automated Image Annotation Using Global Features and Robust Nonparametric Density Estimation" (PDF). Int'l Conf on Image and Video Retrieval (CIVR, Singapore, Jul 2005). Archived from the original (PDF) on 2005-12-20.
  • Video semantics
N Vasconcelos & A Lippman (2001). "Statistical Models of Video Structure for Content Analysis and Characterization" (PDF). IEEE Transactions on Image Processing. pp. 1–17.
Ilaria Bartolini; Marco Patella & Corrado Romani (2010). "Shiatsu: Semantic-based Hierarchical Automatic Tagging of Videos by Segmentation Using Cuts". 3rd ACM International Multimedia Workshop on Automated Information Extraction in Media Production (AIEMPro10).
  • Image Annotation Refinement
Yohan Jin; Latifur Khan; Lei Wang & Mamoun Awad (2005). "Image annotations by combining multiple evidence & wordNet". 13th Annual ACM International Conference on Multimedia (MM 05). pp. 706–715.
Changhu Wang; Feng Jing; Lei Zhang & Hong-Jiang Zhang (2006). "Image annotation refinement using random walk with restarts". 14th Annual ACM International Conference on Multimedia (MM 06).
Changhu Wang; Feng Jing; Lei Zhang & Hong-Jiang Zhang (2007). "content-based image annotation refinement". IEEE Conference on Computer Vision and Pattern Recognition (CVPR 07). doi:10.1109/CVPR.2007.383221.
Ilaria Bartolini & Paolo Ciaccia (2007). "Imagination: Exploiting Link Analysis for Accurate Image Annotation". Springer Adaptive Multimedia Retrieval. doi:10.1007/978-3-540-79860-6_3.
Ilaria Bartolini & Paolo Ciaccia (2010). "Multi-dimensional Keyword-based Image Annotation and Search". 2nd ACM International Workshop on Keyword Search on Structured Data (KEYS 2010).
  • Automatic Image Annotation by Ensemble of Visual Descriptors
Emre Akbas & Fatos Y. Vural (2007). "Automatic Image Annotation by Ensemble of Visual Descriptors". Intl. Conf. on Computer Vision (CVPR) 2007, Workshop on Semantic Learning Applications in Multimedia. doi:10.1109/CVPR.2007.383484. hdl:11511/16027.
  • A New Baseline for Image Annotation
Ameesh Makadia and Vladimir Pavlovic and Sanjiv Kumar (2008). "A New Baseline for Image Annotation" (PDF). European Conference on Computer Vision (ECCV).

Simultaneous Image Classification and Annotation

Chong Wang and David Blei and Li Fei-Fei (2009). "Simultaneous Image Classification and Annotation" (PDF). Conf. on Computer Vision and Pattern Recognition (CVPR).
  • TagProp: Discriminative Metric Learning in Nearest Neighbor Models for Image Auto-Annotation
Matthieu Guillaumin and Thomas Mensink and Jakob Verbeek and Cordelia Schmid (2009). "TagProp: Discriminative Metric Learning in Nearest Neighbor Models for Image Auto-Annotation" (PDF). Intl. Conf. on Computer Vision (ICCV).
  • Image Annotation Using Metric Learning in Semantic Neighbourhoods
Yashaswi Verma & C. V. Jawahar (2012). "Image Annotation Using Metric Learning in Semantic Neighbourhoods" (PDF). European Conference on Computer Vision (ECCV). Archived from the original (PDF) on 2013-05-14. Retrieved 2014-02-26.
  • Automatic Image Annotation Using Deep Learning Representations
Venkatesh N. Murthy & Subhransu Maji and R. Manmatha (2015). "Automatic Image Annotation Using Deep Learning Representations" (PDF). International Conference on Multimedia (ICMR).
  • Holistic Image Annotation using Salient Regions and Background Image Information
Sarin, Supheakmungkol; Fahrmair, Michael; Wagner, Matthias & Kameyama, Wataru (2012). Leveraging Features from Background and Salient Regions for Automatic Image Annotation. Journal of Information Processing. Vol. 20. pp. 250–266.
  • Medical Image Annotation using bayesian networks and active learning
N. B. Marvasti & E. Yörük and B. Acar (2018). "Computer-Aided Medical Image Annotation: Preliminary Results With Liver Lesions in CT". IEEE Journal of Biomedical and Health Informatics.
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