In an earlier post, I had described how DBSCAN is way more efficient(in terms of time) at clustering than K-Means clustering. It turns out that there is a modified K-Means algorithm which is far more efficient than the original algorithm. The algorithm is called Mini Batch K-Means clustering. It is mostly useful in web applications […]

I recently came across a wonderful paper by Jitendra Malik, one of the pioneers of Computer Vision. It is called ‘Shape Matching and Object Recognition using Shape Contexts’. It describes how objects can be characterized by their shape, and how this description can be used effectively to match and recognize objects. In this post I […]

Feature Detection is basically finding key points or pixels in the image. An immediate question would be to ask what a key point is. A key point is a point which is unique in the local area around it and can be found and matched to a corresponding point in another image. Aperture Problem The […]

We will try to improve the efficiency of the Uniform Cost Search algorithm by using heuristics which we discussed in the previous post. By improving the efficiency I mean that the algorithm will expand less of the search tree and will give the optimal result faster. We start with the same search problem and search […]

Search heuristics are important in improving the efficiency of a search problem. This post will describe what heuristics are and why they are used in artificial intelligence applications. I will cover some of the standard topics in heuristics which are admissible and consistent heuristics. What are heuristics? Heuristics can be said to be estimates of […]

In this post I will talk about the Uniform Cost Search algorithm for finding the shortest path in a weighted graph. Given below are the diagrams of example search problem and the search tree. If you donâ€™t know what search problems are and how search trees are created visit this post. Uniform Cost Search Uniform […]