Adaptive metric learning for saliency detection

OBJECTIVE

Using GML & SML technique, salient features of objects from the background of an image is detected very efficiently.

ABSTRACT

The GML and SML together and experimentally find the combining result which would work well.

Based on low-level features, we will find a super pixel wise image without loosing pixels.

Fisher vector coding approach is a better distinguish for salient objects from the background detection.

EXISTING SYSTEM

The saliency of a super pixel can be estimated by the Euclidean distance from the most certain foreground and background seeds.

DISADVANTAGES

Not in accurate manner

Processing is so lengthy

No key generation will be possible

PROPOSED SYSTEMS Instead of measuring distance on the Euclidean

space, a learning method can be presented based on two complementary Mahalanobis distance metrics:

1) Generic metric learning (GML) 2) Specific metric learning (SML).

GML aims at the global distribution of the whole training set, while SML considers the specific structure of a single image.

Fig. The comparison between the Euclidean distance space and the Mahalanobis distance space

ADVANTAGES

Key generation.

Matching of the pixels will be done easily.

Thank you