IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v2y2017i2idhcseit2174123.html

Automatic Image Caption Generation

Author

Listed:
  • Kavitha S
  • Keerthana V
  • Bharanidharan A

Abstract

Recent work in machine learning uses an attention-based model, which automatically learns to predict the content of image. Automated Image Caption generation uses a type of artificial intelligence called deep learning to generate appropriate caption for the images. It also provides the best words to explain the image entirely. The textual description of the image would be generated after processing the image and its visual content has been analyzed. Caption generation can talk about the features of the scene and how the people and the objects in the image interact. The shape information of an image is mostly enclosed in edges. So, it is necessary to detect edges for an input image, by using certain filters and by enhancing those areas of image which contains edges, sharpness of image will increase and image will become clearer. The filter used here is sobel operator. Edge detection is used to extract features from the image and based on these features, caption will be generated which depicts the image. To convert these features into a meaningful caption, KNN classifier is used.

Suggested Citation

  • Kavitha S & Keerthana V & Bharanidharan A, 2017. "Automatic Image Caption Generation," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(2), pages 537-540, April.
  • Handle: RePEc:jbh:ijsrcs:v2:y2017:i2:id:hcseit2174123
    Note: Article URL: https://ijsrcseit.com/CSEIT2174123
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT2174123
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/paper/CSEIT2174123.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v2:y2017:i2:id:hcseit2174123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.