Are You a Convolution Filter Expert?

Convolution mimics the human visual system on the retina by using local connections between photoreceptive cells and neurons.

A convolution filter is a local filter operation and a local information processing unit.

The bag-of-feature approach is a past effort in object recognition that involves recognizing individual parts of an image, such as the nose, eyes, chin, and mouth, using a filter operation.

<p>The accuracy is about 37%.</p> Signup and view all the answers

<p>The accuracy is up to 78%.</p> Signup and view all the answers

<p>The soft coding function approximates a weighting vector to generate a new test sample from the limited number of k-means cluster centers.</p> Signup and view all the answers

<p>The final step is to subdivide the whole image into quadrants and pool information along these using local sum pooling.</p> Signup and view all the answers

<p>What is the idea behind convolution filters and how are they related to the human visual system?</p> Signup and view all the answers

<p>Convolution filters mimic the local connections between photoreceptive cells and neurons in the human visual system on the retina.</p> Signup and view all the answers

<p>What is the bag-of-feature approach and how is it related to object recognition?</p> Signup and view all the answers

<p>The bag-of-feature approach is the idea that to classify a whole image, we first need to recognize individual parts of the image using some filter operation.</p> Signup and view all the answers

<p>How did past efforts to object recognition use the bag-of-feature approach?</p> Signup and view all the answers

<p>They learned a vocabulary to represent clusters of object parts and then quantized their representation as a vocabulary entry, creating histograms of visual words.</p> Signup and view all the answers

<p>What is the accuracy of directly learning a classifier on the raw pixels of an image?</p> Signup and view all the answers

<p>About 37% accuracy.</p> Signup and view all the answers

<p>What is the accuracy of using a single layer neural network with unsupervised learning with the k-means algorithm?</p> Signup and view all the answers

<p>Up to 78% accuracy.</p> Signup and view all the answers

<p>How do they extract features using the k-means algorithm in the bag-of-feature approach?</p> Signup and view all the answers

<p>They extract partially overlapping 6x6 RGB patches from the images, learn the encoding using k-means clustering and soft coding, and put these visual words together in a histogram and do local sum pooling.</p> Signup and view all the answers

<p>What do the unsupervised learned k-means centers represent?</p> Signup and view all the answers

<p>The most representative patches from the data set without applying any supervised learning, and they look similar to convolution filters.</p> Signup and view all the answers