A Tutorial Review of Automatic Image Tagging Technique Using Text Mining

نویسندگان

  • Sayantani Ghosh
  • Samir Kumar Bandyopadhyay
چکیده

With the advent of time, the number of images being captured and shared online has grown exponentially. The images which are captured are later accessed for the purpose of searching, classification and retrieval operation. Hence these images must be labelled with appropriate words, phrases or keywords so that the requisite operation can be performed efficiently. Automatic Image Tagging is such a technique which associates an appropriate keyword from a given set of words or phrases based on the relevance to the content of the image. This selection of the appropriate keyword can be performed by Text Mining which is concerned with the extraction of appropriate information from a given text. The main objective of this paper is to depict how Text Mining technique can be implemented in the process of Automatic Image Tagging. In order to annotate an image, techniques like Content Based Image Retrieval cane can be used, which emphasises on the content of the image to annotate an image. However due to several constraints of the above mentioned technique, Automatic Image Tagging technique is used which chooses a tag from a given set of tags to annotate an image. The selection of the appropriate tag can be implemented using the Classification logic of the Text Mining Algorithm that assigns the given set of keywords or tags to some predefined classes. In this way the most relevant tags can be selected assigned to the given image.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Text Mining for Automatic Image Tagging

This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on two datasets, we show that our methods exceed competitive baselines by a large margin, and compare favorably with the stateof-the-art that uses both textual and image features.

متن کامل

Improvement of generative adversarial networks for automatic text-to-image generation

This research is related to the use of deep learning tools and image processing technology in the automatic generation of images from text. Previous researches have used one sentence to produce images. In this research, a memory-based hierarchical model is presented that uses three different descriptions that are presented in the form of sentences to produce and improve the image. The proposed ...

متن کامل

A survey on Automatic Text Summarization

Text summarization endeavors to produce a summary version of a text, while maintaining the original ideas. The textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such massive amount of data, in order to extract the useful information, is a major undertaking and requires an automatic mechanism to aid with the extant repository of informa...

متن کامل

سیستم برچسب گذاری اجزای واژگانی کلام در زبان فارسی

Abstract: Part-Of-Speech (POS) tagging is essential work for many models and methods in other areas in natural language processing such as machine translation, spell checker, text-to-speech, automatic speech recognition, etc. So far, high accurate POS taggers have been created in many languages. In this paper, we focus on POS tagging in the Persian language. Because of problems in Persian POS t...

متن کامل

بهبود خلاصه سازی خودکار متون فارسی با استفاده از روش‌های پردازش زبان طبیعی و گراف شباهت

A significant amount of available information is stored in textual databases which contains a large collection of documents from different sources (such as news, articles, books, emails and web pages). The increasing visibility and importance of this class of information motivates us to work on having better automatic evaluation tools for textual resources. The automatic summarization of tex...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014