Image Processing Reference
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(CBIR), however, recent research reveals a significant gap between image annotation interpret-
ered based on visual features and image semantics understandable by humans.
A glimpse of related studies show that the typical method of bridging the semantic gap
is through the automatic image annotation (AIA) which extracts semantic features using ma-
chine learning techniques including support vector machine (SVM) [ 1 , 2 ], Bayesian [ 3 ] and
selforganizing feature map [ 4 , 5 ] . In addition, various text processing techniques that sup-
port content identification based on word cooccurrence, location, and lexical-chained concepts
have been elaborated in [ 6 - 8 ]. However, the aforementioned techniques suffer the drawbacks
of heavy computation for multidocument processing, which will consume lots of memory and
may incur run-time overhead. Furthermore, these feature-based or text processing techniques
tend to assign the same annotation to all the images in the same cluster without considering
the latent semantic anecdotes of each image. Thus, many relevant images can be missed from
the retrieval list if a user does not type the exactly right keyword. To alleviate this problem,
we propose a customized, relatively light computation way of anecdote identification, namely
“Chinese lexical chain processing (CLCP).” The CLCP method was to extract meaningful con-
catenated words based on lexical chain (LC) theory from the image-resided webpage that all
lows sharing characters/words and facilitating their use at fine granularities without prohibit-
ive cost. Results demonstrated that applying our method can successfully generate anecdotal
descriptors as image annotations. The precision rate achieves 84.6%, and the acceptance rates
from experts and users reach 84% and 76.6%, respectively. The performance testing was also
very promising.
The remainder of the chapter is organized as follows. Section 2 presents the related work
including AIA, keyword extraction, and LC. Then, we address the intent of our experiment in
Section 3 . The experimental result and evaluation method are described in Section 4 . Finally,
in Section 5 , we draw conclusions and suggest future work.
2 Literature background
2.1 Automatic Image Annotation
There have been a number of models applied for image annotation. In general, image annota-
tion can be categorized into three types: retrieval-based, classification-based, and probabilistic-
based [ 7 ] . The basic notion behind retrieval-based annotation is that semantic-relevant images
are composed of similar visual features. CBIR has been proposed in 1992 [ 9 ]. Since then, more
and more studies annotated the images based on this method [ 10 ] .
CBIR is applied by the use of images features, including shape, color, and texture. Once im-
ages are classified into different categories, each category is annotated with a concept label
such as bird, cat, mammal, and building. However, this method is limited by the training data
set and the hidden semantics or abstract concepts can't be extracted because the keywords are
conined to predefined terms. Consequently, the results of CBIR are usually not satisfactory.
The second type, also known as the supervised learning approach, treats annotation as
classiication using multiple classifiers. The images are classified based on the extracted fea-
tures. This method processes each semantic concept as an independent class, and assigns each
concept as one classifier. Bayesian [ 3 ] and SVM [ 11 ] are the most often used approaches.
The third type is constructed by estimating the correlations between images and concepts
with a particular emphasis on the term-term relationship and intends to solve the problem of
“synonym” and “homograph.” Frequent used approaches include cooccurrence model [ 12 ] ,
LSA [ 5 ], PLSA [ 13 ] , and hidden Markov model (HMM) [ 14 ] . Notwithstanding the efforts made
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