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Fig. 8.17
The re-ranking procedure for modification of a query for video retrieval on a peer-to-
peer network; (
a
) the user node sends query packets to its peers in the neighborhood community;
(
b
) the query is modified by the peer nodes and sent back to the user node; (
c
) the user node gathers
the modified queries and computes the second modified query and sends it to the peer nodes; (
d
)
the final retrieval results (video files) are sent to the user node
l
x
i
N
l
x
i
can be obtained. In this way, the label
l
j
represents the occurrence of template
g
j
in the input video. Thus, the term-vector
model [
360
] can be applied to the resulting set of labels. The number of times
g
j
present in the video can be viewed as the
term frequency
(TF)
.
We can formally
formulate the corresponding term-weighting vector as:
v
,...,
1
,
l
j
∈{
1
,
2
,...,
J
},
j
∗
,
j
∗
,
ʷ
1
i
=
t
where
w
j
represents the multiplication of the term frequency (TF) and the inverse
term frequency (ITF) of the
j
-th template
g
j
for the input video.
=[
w
1
,...,
w
j
,...,
w
J
]
8.5.3
Re-ranking Approach to P2P Video Retrieval
Each node in the P2P network shown in Fig.
8.17
stores a collection of video
clips, each of which is indexed by a weight vector. All the members of the
neighborhood community are assuming the use of a single set of visual templates
C
J
. The peers may have a different number of video files.
Figure
8.20
illustrates the zoom version of Peer
n
. Video indexing within this
peer can be viewed as a network of three layers: (first) the query vector, (second)
=
g
j
|
j
=
1
,
2
,...,
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