| |
We present a generic detection/localization algorithm capable of
searching for a visual object of interest without training. The
proposed method operates using a single example of an
object of interest to find similar matches; does not require prior
knowledge (learning) about objects being sought; and does not
require any pre-processing step or segmentation of a target image.
Our method is based on the computation of local regression kernels
as descriptors from a query, which measure the likeness of a pixel
to its surroundings. Salient features are extracted from said
descriptors and compared against analogous features from the target
image. This comparison is done using a matrix generalization of the
cosine similarity measure. We illustrate optimality properties of
the algorithm using a naive-Bayes framework. The algorithm yields a
scalar resemblance map, indicating the likelihood of similarity
between the query and all patches in the target image. By employing
nonparametric significance tests and non-maxima suppression, we
detect the presence and location of objects similar to the given
query. The approach is extended to account for large variations in
scale and rotation. High performance is demonstrated on several
challenging datasets, indicating successful detection of objects in
diverse contexts and under different imaging conditions.
System Overview
Experimental Results
See more details and examples in the following papers
.
Go to action recognition page
Go to saliency detection page
|
