In this paper, we consider the combined problem of distinguishing classes from the background and from each other, and
propose an improved framework based on the previous state-of-the-art approaches. In the process of building ECOC
(Error Correcting Output Coding) matrix (also called as sharing matrix), we adopt an encoding rule of one-versus-all,
and maximize Hamming distance in categories as far as possible through heuristic search in sharing-code maps (i.e.,
layer joint boosting). Then the final classifier is responsible for detection, and ECOC matrix for recognition. In order to
make full use of the output of the final classifier and its corresponding ECOC matrix, the following measures are worth
considering: Firstly, a logistic function of the output mentioned above is used for a posterior probability of each
codeword. Therefore the identified class label is the one corresponding to the codeword of Maximum a posteriori
(MAP). Secondly, a similarity measurement utilizing the confusion matrix is advanced to focus on the similarities
between classes. Thirdly, for the purpose of adaptive adjustment in Hamming distance, we change the subsequent search
coding method according to the confusion matrix until the training errors are convergent. The experimental results
illustrate the effectiveness of the proposed approach.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.