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Logo Recognition by Dan Chen, Lizhe Wang, Jingying Chen

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9
Conclusion
CONTENTS
9.1 Book summary ............................................................ 130
9.2 Contribution .............................................................. 130
9.3 Future work ............................................................... 131
9.4 Book conclusion ........................................................... 132
Logo recognition is of great interest in the document and shape matching do-
main. Logos can act as a valuable means to identifying sources of documents.
However, logos are 2D shapes of varying complexity, with interior and exte-
rior contours that are not necessary connected. Hence the recognition process
seems to be difficult because of its complexity. Researchers have investigated
the problem of logo recognition [46, 189, 91, 234, 213, 36, 143]. Although some
very effective results have been found for clean logos, they can hardly be ro-
bust with corrupted logos, such as strips obstructing the logo in unpredictable
positions. Other research on logo recognition by neural networks [26, 25, 73]
can deal with noisy logos; however, it requires a lot of training before it can
be used.
In order to provide better distinctive capability for recognizing logos under
adverse conditions such as different scale/orientation, broken lines and added
noise and occlusion, this book proposes a new logo recognition approach, i.e.,
a hybrid of structural feature based and template based techniques, which
includes two aspects:
Consistent logo representation: Extract local line pattern features which
are invariant to scale, orientation, translation and reasonable amounts of
noise and occlusion.
Effective logo recognition: Find a suitable similarity/dissimilarity mea-
sure which is efficient to compute, tolerates reasonable amounts of noise
and occlusion and degrades gracefully as these tolerances are exceeded.
129
130 Logo Recognition: Theory and Practice
9.1 Book summary
This book has investigated an approach (combining the structural and tem-
plate matching techniques) that can represent and recognize complex shapes
(i.e., logos) under noisy conditions. It can tolerate reasonable amounts of noise
and occlusion, and degrade gracefully as these tolerances are exceeded. On the
other hand, since the method is based on line segments, it is easy to imple-
ment and demands less storage space. This work has involved the following
investigations:
Polygonal approximation: Transform raw logo images into consistent Line
Segment Maps (LSM).
Indexing: Investigate and search for effective line pattern features that
can be used to index the database to generate a moderate number of
likely models with respect to a test image.
Matching: Propose an improved Line Segment Hausdorff Distance (LHD)
measure to screen further and generate the best matches.
In this book, numerous improvements have been made, including a con-
sistent feature point detection method, a robust normalization process, an
effective indexing approach and a reliable MLHD algorithm that is the most
important part in the proposed system. An in-depth study of the proposed
technique has also been carried out on logos. The test images come from seven
sources, i.e., regenerated, strip corrupted, partially occluded, mixed noise (i.e.,
spot and white Gaussian noise) corrupted, cylinder projected, skewed and for-
eign logos. Encouraging results have been observed. These results show that
the proposed technique is invariant to scale, orientation, and translation and
tolerates reasonable amounts of noise and occlusion. It degrades gracefully as
these tolerances are exceeded. Compared with other works on logo recognition,
no existing technique can tolerate as many distortion types as that proposed
in this book. On the other hand, the proposed method will eventually fail
when severe distortion occurs, or when the normalization process fails to find
a single reference line. Nonetheless, to the best of our knowledge, other ap-
proaches cannot tackle such distorted images as well. The proposed approach
can work reasonably well as long as one suitable reference line can be found,
while other approaches are not likely to succeed.
9.2 Contribution
This book has made the following contributions:

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