Randomized Tree

Occlusions, cluttered backgrounds, and viewpoint or orientation changes that occur in real-world images motivated the development of object recognition or image retrieval methods that model image appearances locally by using the so-called “local-features”. e.g., the neighbourhood of corners, lines/edges, contours or homogenous regions capture interesting aspects of images to classify or compare them.

A single detector might not capture enough information to distinguishing all images!

Using several detectors on a uniform grid or even randomly. e.g., subwindow random sampling scheme: square patches of random sizes are extracted at random locations in image, resized by bilinear interpolation to a fixed-size 16×16, described by HSV value for color images (768d), or gray intensities to graylevel images(256d).

Extraction of random subwindows described by raw pixel values – randomized trees:

The method recursively partitions the training sample of subwindows by randomly generated tests.

  • Each test is chosen by selecting a random pixel component among the 768 wubwindows descriptors, and a random cut-point in the range of variation of the pixel component in the subset of subwindows associated to the node to split.
  • Each test associated to an internal node of a tree, just simply compares a pixel component to a numerical threshold
  • The development of a node is stopped: either all descriptors are constant in the leaf, or the number of subwindows in the leaf is smaller than a predefined threshold.
  • A number of such trees are grown from the training sample.

There exists a number of indexing techniques based on recursive partitioning.

  • Use of an ensemble of trees
  • Random selection of tests in place of more elaborated splitting strategies: based on a distance computed over the whole descriptors taken at the median of the pixel component whose distribution exhibits the greatest spread.
  • Computational complexity is essentially independent of the dimensionality of the feature space, O(Nlog(N)) in the number of the subwindows. Like other tree methods

Indexing with totally randomized trees, is also related to the random projection method of locality-sensitive hashing (LSH), which used to approximate nearest neighbour searches.

  • The assumption is that nearby objects are more likely to be hashed to the same bucket than distance ones: Once a new object is hashed to a bucket, a similarity measure is computed between this object and all reference objects which were hashed in the same bucket during indexing phase

Extra-Trees: Totally randomized trees

Use of extremely randomized trees to build visual vocabularies before applying the SVM classifier, binary encoding in tree leaves is not good, real-valued similarity measure preform image classification from labeled images.

Image similarities from tree ensembles

  1. Two subwindows are very similar of they fall in a same leaf that has a very small subset of training subwindows.
  2. Two subwindows are similar if they are considered similar by a large proportion of the trees
  3. The smallest number of subwindows – the bigger, more subwindows falling into the same leaf which yields a higher similarity, and the number of trees – controls the smoothness of the similarity

The similarity between two images

the average similarity between all pairs of their subwindows, although finite, the number of different subwindows of variable size and location which can be extracted from a given image is in practice very large, thus we propose to estimate by extracting at random from each image an a priori fixed number of subwindows.

Image retrieval

Given a set of Nr reference images, to find images from this set which are most similar to a query image Iq:

  1. Randomly extract Nls subwindows of variable size and location from each reference image, resize them to 16×16, and grow an ensemble of totally randomized trees from them.
  2. Compute k(Iq, Ir) for each reference image Ir and returns the N most similar images to the query according to these similarities.

 

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

生活在西班牙

自己动手丰衣足食

BlueAsteroid

Just another WordPress.com site

Jing's Blog

Just another WordPress.com site

Start from here......

我的心情魔方

天才遠私廚

希望能做一個分享各種資訊的好地方

语义噪声

西瓜大丸子汤的博客

笑对人生,傲立寰宇

Just another WordPress.com site

Where On Earth Is Waldo?

A Project By Melanie Coles

the Serious Computer Vision Blog

A blog about computer vision and serious stuff

Cauthy's Blog

paper review...

Cornell Computer Vision Seminar Blog

Blog for CS 7670 - Special Topics in Computer Vision

datarazzi

Life through nerd-colored glasses

Luciana Haill

Brainwaves Augmenting Consciousness

槑烎

1,2,∞

Dr Paul Tennent

and the university of nottingham

turn off the lights, please

A bunch of random, thinned and stateless thoughts around the Web

%d bloggers like this: