These two operations have different goals, inputs and outputs.

Thresholding: Works from grayscale images to separate "intelligently" pixels into 2 populations (black and white) by finding a specific gray value as a threshold.

Labeling: Works from an already segmented image (as obtained from thresholding) and assigns a unique label to each isolated components. It is therefore not a segmentation method. These components can later be analyzed individually by other image processing algorithms.

I suggest you look at any general book on image processing to see visual examples of these techniques and common implementation methods.  If you are doing C or C++, OpenCV is a great place to look for robust and high performance implementations of MANY computer vision algorithms. Otherwise, Matlab, Maple or Mathematica all have interactive environments where you can play with these algorithms.

they are totally different techniques

 typically a black  and white binary image is obtained after the operation of thresholding

for more threshold methods see: Comparison study between different automatic threshold algorithms for motion detection

the components labeling comes after thresholding and consists in assigning a unique label to each maximal connected region of foreground pixels.

to understand labeling with example see:

http://www.labbookpages.co.uk/software/imgProc/blobDetection.html

As mentioned before, labelling comes after thresholding or segmentation. Segment or threshold would concern color or grey level images and consists of separating different regions of the image. The labelling consists of identifying the different regions using different labels (numbers, colors, etc.). The same label is affected to the connected (adjacent) pixels in 2D or voxels in 3D. Connected components might be identified using different rules such as 6/26 connectivity in 3D. See the following reference: [A. Rosenfeld , A. C. Kak (1982), Digital Picture Processing, Academic Press, Inc., ISBN 0-12-597302-0].