A review on automatic image annotation techniques

By Dengsheng Zhang, M. Islam, Guojun Lu. Pattern Recognition, 2012

565 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/84c7b6dcf2424795928749c0e76b4fa4c8f7b745

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Alberto Fernández
2. Edurne Barrenechea Tartas
3. Francisco Herrera
4. Gilles Louppe
5. Mikel Galar
6. Dong-ping Tian
7. Patrick Mäder
8. Jana Wäldchen

In Media Technology

1. Francisco Herrera
2. Gilles Louppe
3. Dong-ping Tian
4. Patrick Mäder
5. Jana Wäldchen

Citations in the corpus

• A novel ensemble method for classifying imbalanced data (2015, background): “The class imbalance problems have been reported to occur in a wide variety of realworld domains, such as facial age estimation [1], detecting oil spills from satellite images [2], anomaly detection [3], identifying fraudulent credit card transactions [4], software defect prediction [5], and image an”
• Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review (2017, background): “A number of general color descriptors have been proposed in the field of image recognition, e.g., color moments (CM), color histograms (CH), color coherence vector , and color correlogram [153].”
• EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013, background): “The great attention that this problem has attracted in the literature is understandable due to the large amount of real-world classi fi cation problems suffering from these conditions, such as anomaly detection [30], image annotation [59] or facial age estimation [9], among many others.”
• A Review on Image Feature Extraction and Representation Techniques (2013, methodology): “For more details of them, please refer to reference [17] and the corresponding original papers.”
• Understanding Random Forests: From Theory to Practice (2014, background): “Another example is image annotation, in which the goal is to annotate (i.e., classify) every pixel of an image in order to identify regions or objects (see [Zhang et al., 2012]).”