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Discriminant Saliency Results

This page presents a collection of results obtained with the discriminant saliency model. We briefly review the model and then show how it performs on texture and object databases.

Discriminant Saliency Model

Discriminant saliency is defined with respect to a recognition problem: the salient features of a given visual class are the features that most distinguish it from all other visual classes. As shown in the following figure, the saliency detection model consists of two steps. The first is the selection of discriminant features. The second consists of a biologically inspired model for 1) translating the image responses of those features into a saliency map (which assigns a degree of saliency to each image pixel) and 2) using that map to determine the most salient image locations.

Examples from the Brodatz texture database

Examples of saliency maps for various textures are shown here. It can be seen from these examples that  discriminant saliency can 1) ignore highly textured backgrounds in favor of more salient foreground objects, and 2) detect as salient a wide variety of shapes, and contours of different crispness and scale, or even texture gradients.

Examples from the Caltech object database

The following are saliency maps and top salient locations detected by discriminant saliency on various images from the Caltech object database. For comparison, we also present the salient locations detected by other methods that are popular in the saliency literature (Multiscale Harris, and Scale Saliency).

Original Image
Saliency Maps by Discriminant Saliency
Salient Locations by Discriminant Saliency
Salient Locations by Scale Saliency
Salient Locations by Multiscale Harris

Robustness on the Caltech object database

Here are more saliency detection results on Caltech Database. These are intended to give an idea of the robustness of discriminant saliency.

Robustness to 3-D Rotation

The following are examples from the Columbia Object Image Library (COIL), which is a good dataset for testing the robustness of salient locations to 3-D rotation. It can be seen that discriminant saliency declares as salient neighborhoods whose appearance is consistent among different views of an object. In these examples, the rotation between adjacent images is of 5 degrees, and the top 10 salient locations are identified.


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