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Multi-Label Image Categorization With Sparse
Factor Representation
Abstract—The goal of multilabel classification is to reveal the underlying label correlations to boost the accuracy of
classification tasks. Most of the existing multilabel classifiers attempt to exhaustively explore dependency between
correlated labels. It increases the risk of involving unnecessary label dependencies, which are detrimental to classification
performance. Actually, not all the label correlations are indispensable to multilabel mode l. Negligible or fragile label
correlations cannot be generalized well to the testing data, especially if there exists label correlation discrepancy between
training and testing sets. To minimize such negative effect in the multilabel model, we propose to learn a sparse structure
of label dependency. The underlying philosophy is that as long as the multilabel dependency cannot be well explained, the
principle of parsimony should be applied to the modeling process of the label correlations. The obtained sparse label
dependency structure discards the outlying correlations between labels, which makes the learned model more
unrealizable to future samples. Experiments on real world data sets show the competitive results compared with existing
algorithms.

2. Existing method:
Most of existing multi- label classifiers assume that the labels are ubiquitously correlated with
each other with densely correlative dependency. However, it does not hold since there exist
labels with negligible or quite weak correlations among them. For example, it is intuitive that
“bird” and “train” have no obvious correlations between them. Incorporating such outlying label
correlations can increase the risk of imposing incorrect correlative constraints between labels that
are not generalizable to testing samples. From the perspective of modeling, it unnecessarily
increases the complexity of the multi- label model, which makes the label correlations in the

3. model prone to over- fitting into those of the noisy training set. This problem is extremely serious
if there exists remarkable label correlation discrepancy between the training and test sets.
Proposed method:
Supervised learning (SL), consists of training and prediction phases, which requires a batch of
training examples annotated by a set of semantic labels to establish a learner of the satisfactory
generalization capability. To work toward the goal, various methods for multi- label classification
have been proposed according to different problem settings. Chen et al. proposed a supervised
nonnegative matrix factorization (NMF) approach for both image classification and annotation
with the aid of label information, in which two supervised nonnegative matrix factorizations are
combined together to identify the latent image bases and represent the training images in the
bases space. Hypergraph spectral learning is utilized in and for multi- label classification, where a
hypergraph is constructed to exploit the correlation information among different labels. Han et
al. proposed a multi- task sparse discriminate analysis approach that formulates multi- label
prediction as a quadratic optimization problem. Structured visual feature selection and the
implementation of hierarchical correlated structures among multiple tags are exp lored together in
to boost the performance of image annotation.

4. 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: