01-08-2012, 10:47 AM
Automatic Image Annotation and Retrieval using Cross Media Relevance Models
Automatic Image.pptx (Size: 90.47 KB / Downloads: 27)
Concept Based Query Expansion
Probabilistic query expansion model
Based on a similarity thesaurus
Selection & weighting of additional search terms
Works with small databases
Ontology-Based Query Expansion Widget for information Retrieval
Potential loss in precision
If a query concept has lots of subconcepts, the expanded query string may become inconveniently long
The used HTTP server, database system or other software components may set limits to the length of the query string
GREY SCALE ALGORITHM
Read all pixel values of uploaded input images
Read width & height of all input images
Read pixel values of images from top-left to bottom value
Place each pixel values into a temporary buffer matrix
Find out RGB color format of each pixel values
Convert RGB color format into black & white format
Place temporary buffer containing black & white format to greyscale matrix.
EDGE DETECTION ALGORITHM
Read image content from grayscale matrix
Start tracing pixel values from top-left to bottom-right to find out intensity & threshold values
Trace all pixel values from top-left to bottom –right to find changes in color intensities
Make a starting vector point of edge by comparing changes in pixel intensities from neighbour pixel
K-MEANS ALGORITHM
It is used while training the images
It uses the properties of all the images & which helps in making a structural format
Read the images from top-left to bottom-right
Get the data from edge detection algorithm
Read the intensity threshold values of the images by counting all pixels
Repeat first 4 steps for all images