To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

1. GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
Multilabel Image Classification via High-Order
Label Correlation Driven Active Learning
Abstract—Supervised machine learning techniques have been applied to multilabel image
classification problems with tremendous success. Despite disparate learning mechanisms, their
performances heavily rely on the quality of training images. However, the acquisitio n of training
images requires significant efforts from human annotators. This hinders the applications of
supervised learning techniques to large scale problems. In this paper, we propose a high-order
label correlation driven active learning (HoAL) approach that allows the iterative learning
algorithm itself to select the informative example- label pairs from which it learns so as to learn
an accurate classifier with less annotation efforts. Four crucial issues are considered by the
proposed HoAL: 1) unlike binary cases, the selection granularity for multilabel active learning
need to be fined from example to examplelabel pair; 2) different labels are seldom independent,
and label correlations provide critical information for efficient learning; 3) in additio n to pair-wise
label correlations, high-order label correlations are also informative for multilabel active
learning; and 4) since the number of label combinations increases exponentially with respect to
the number of labels, an efficient mining method is required to discover informative label

2. correlations. The proposed approach is tested on public data sets, and the empirical results
demonstrate its effectiveness.

3. Existing method:
A large portion of the existing active learning techniques are designed for myopic active
learning: only one example
is selected for annotation at each learning iteration, and the classifier is updated every time when
a new annotated example becomes available. Such setting hinders the adoption of parallel
annotation systems, and incurs heavy computational cost on classifier updates, thereby
preventing active learning being utilized on large scale real world applications, such as automatic
Internet image annotation.
Proposed method:
although several algorithms have been proposed to consider label correlations for multi- label
classification, most
of them only exploit low order label correlations (e.g., pairwise label correlations) due to the
computational complexity. The search efforts increase exponentia lly when one more order is
considered for searching informative label correlations. Nevertheless, high order label
correlations often reveal vital information for efficient active learning. An efficient method is in
demand for discovering useful high order label correlations. Fourthly, finding informative label
correlations is not trivial, especially for higher order label correlations. Uninformative label
correlations could deteriorate learning performance. Therefore, proper measurement for the
informativeness of

4. label correlations is important. And the discovery process for informative correlations should be
efficient, especially when the size of the dateset is huge.
Merits:
1. Better PSNR values
2. Output image more enhancement.
3. Low BER rate
Demerits:
1.noise level is very high
2. restoration process time is very high.

5. Results: