A survey on automatic image annotation

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Rad Roya; Jamzad Mansour

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Journal Paper

Paper Information

Journal: MACHINE VISION AND IMAGE PROCESSING Year:2019 | Volume:6 | Issue:1 Page(s): 1-17

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Information Journal Paper

Title

A survey on automatic image annotation

Author(s)

Rad Roya | Jamzad Mansour | Issue Writer Certificate

Keywords

Automatic Image Annotation

AIA

Image Retrieval

Feature Extraction

Abstract

Today, with the development of technologies to capture and share images, the number of digital images has increased significantly. The management of this volume of images requires an efficient system to review, classify, search and retrieve the images. New generations of Image Retrieval systems usually take one or a few keywords from the user to retrieve images with visual content related to that keywords. A mechanism that can automatically describe the content of an image (like a human) can increase the efficiency of these systems. Automatic Image Annotation or AIA is a professional method to express the content of images by keywords or tags. AIA systems, investigate the relationship between the meaning of a text and low-level image features by using machine learning techniques. They automatically assign some tags to images to facilitate fast search based on image contents. In this paper, we explain the different steps to implement an AIA system, review the related works and express the problems and challenges in designing such systems. We also introduce several datasets suitable for AIA systems.

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APA: Copy

Rad, Roya, & Jamzad, Mansour. (2019). A survey on automatic image annotation. MACHINE VISION AND IMAGE PROCESSING, 6(1 ), 1-17. SID. https://sid.ir/paper/360081/en

Vancouver: Copy

Rad Roya, Jamzad Mansour. A survey on automatic image annotation. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2019;6(1 ):1-17. Available from: https://sid.ir/paper/360081/en

IEEE: Copy

Roya Rad, and Mansour Jamzad, “A survey on automatic image annotation,” MACHINE VISION AND IMAGE PROCESSING, vol. 6, no. 1 , pp. 1–17, 2019, [Online]. Available: https://sid.ir/paper/360081/en

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