5. form a single a N-dimensional vector,

5. Histogram Analysis

Color
histograms are one of the oldest and best known global feature used in image
retrieval 17. Video is sequence of frames captured by camera. Videos are
sequence of shots & shots are sequence of frames. Distinct type of bodies
can exist between shot. It is impossible for human eyes to detect it openly.
Research has been done on automatic content analysis and segmentation of video.
We can perform a histogram analysis of the RGB features of every frame
analyzed. The histogram analysis is done to find out the number of frames with
the same histogram as compared to others. This is considered to be similarity
of the frames. Cosine techniques measure can be used for detecting histogram
changes in sequence frames.

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• Histogram Creation: The procedure used collates consecutive frames based
upon 3 64-bit histograms (1 of luminance, and 2 of chrominance). These three
histograms are then put series of interconnected events to form a single a
N-dimensional vector, where N is the total number of bins in all three
histograms (in our case N=192).

 

• Cosine Similarity Measure:  The dissimilarity
analogue of the cosine measure is used for comparing the histograms of adjacent
frames. The two N-dimensional vectors, A and B, represent the color signatures
of the frames. The distance Dcos(A,B) between vectors A and B is given by, 

Dcos
(A,B) = 1-?i=1N(ai.bi)  / (?i=1N ai2 .
?i=1N bi2) 1/2

Where ai is one bin in A and bi is the
corresponding bin in B. It can be seen that cosine measure is basically the dot
product of two unit vectors. The result is the cosine of the angle between the
two vectors subtracted from one. Hence a small value for Dcos indicates that
the frames being considered are similar, while a large Dcos value indicates
dissimilarity. A high cosine value can indicate one of two things. Firstly, it
can (and should) signal that a shot boundary has occurred. Secondly, it can be
the result of ‘noise’ in the video sequence, which may be caused by fast camera
motion, a change in lighting conditions, computer-generated effects, or
anything that causes a perceptual change in the video sequence without being an
actual shot boundary. As can be seen, the algorithm used is quite easy compared
to some previously published examples. This is representative of the fact that
the system is currently a working-progress. Also, previous studies have shown
that simpler algorithms often outperform more complex ones on large
heterogeneous video test sets, due to the absence of “hidden” variables and
simpler relationships between threshold settings and results.