This is a good question.

In terms of the first question, a semantic feature describes the visual content of an image by correlating low level features such as colour, gradient orientation with the content of an image scene.   For example, correlate an extracted color such as blue with the sea or sky, white with a building, and so on. For more about this, see

Imaging and vision laboratory:

https://www.disco.unimib.it/upload/gestioneFiles/docs/LongPres/2010/IVL2010.pdf

In terms of the second question, the separation between the visual content in a digital image and semantic descriptions is known as the semantic gap.    For example, consider the separation between edge pixel gradient orientation vs. round or oblong or pointed shape.   For more about this, see

Hao Ma, Jianke Zhu, Michael R. Lyu, Fellow, IEEE, and Irwin King, Senior Member, IEEE, Bridging the Semantic Gap between Image Contents and Tags, JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007:

file:///C:/Users/James%20Peters/Downloads/bridging_double_column.pdf

This is a good question.

In terms of the first question, a semantic feature describes the visual content of an image by correlating low level features such as colour, gradient orientation with the content of an image scene.   For example, correlate an extracted color such as blue with the sea or sky, white with a building, and so on. For more about this, see

Imaging and vision laboratory:

https://www.disco.unimib.it/upload/gestioneFiles/docs/LongPres/2010/IVL2010.pdf

In terms of the second question, the separation between the visual content in a digital image and semantic descriptions is known as the semantic gap.    For example, consider the separation between edge pixel gradient orientation vs. round or oblong or pointed shape.   For more about this, see

Hao Ma, Jianke Zhu, Michael R. Lyu, Fellow, IEEE, and Irwin King, Senior Member, IEEE, Bridging the Semantic Gap between Image Contents and Tags, JOURNAL OF LATEX CLASS FILES, VOL. 6, NO. 1, JANUARY 2007:

file:///C:/Users/James%20Peters/Downloads/bridging_double_column.pdf

Thank you Mr. Peters

I could not download the first file. what is the title of the paper?

low level image features are image characteristics that are captured by computers for the purpose of recognition and classification (such as pixel intensity, pixel gradient orientation, colour), while semantic image features are the features commonly used by human to describe images (objects, actions). traditional image retrieval systems index images using low level features, which in most cases does not correlate to the semantic features thus creating a gap known as the semantic gap often associated with the inability of the image retrieval system to understand and respond to human semantics. Please, have a look at the following papers;

https://www.researchgate.net/publication/265729668_Unsupervised_Classification_of_Images_A_Review

https://www.researchgate.net/publication/268812008_Unsupervised_Image_Classification_by_Probabilistic_Latent_Semantic_Analysis_for_the_Annotation_of_Images

Article Unsupervised Classification of Images: A Review

Conference Paper Unsupervised Image Classification by Probabilistic Latent Se...

Nisreen,

What James sent you is not a paper, it is a PowerPoint presentation (converted to a PDF file) (~57MB). You might find difficulties loading it directly given the Internet speed you have. I will send you the file via email shortly after this.

Dear Nisreen Sulayman,

Look the link.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.8.3816&rep=rep1&type=pdf

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.4405&rep=rep1&type=pdf

Regards, Shafagat