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Do you know of any implementations or improvements of the method of matching images proposed by David Nister and Henrik Stewenius, called "Scalable Recognition with a Vocabulary Tree"? I am trying to implement it and I am having trouble understanding some parts of the algorithm (more specifically, computing the score).
Here is a good implementation of vocabulary tree - libvot. It use the C++11 standard multi-thread library to accelerate the build process so it runs pretty fast.
It uses three steps to build a vocabulary tree. The first step is to build a kmeans tree using sift descriptors. The second step is to build a image database using the vocabulary tree you build in the first step. The third step is to query the image against the image database. Some advanced techniques such as inverted list and L1 distance measure are also reflected in this repository.
Regarding the vocabulary trees, I found this thesis (http://www.tango-controls.org/Members/srubio/MasterThesis-VocabularyTree-SergiRubio-2009.pdf) which implements them in C++/python. However, I can't find the code anywhere, so I contacted the author to get the code but without success til this date.
Furthermore, I found this other implementation (http://www.inf.ethz.ch/personal/fraundof/page2.html), however I was unable to put it to work.
Have you implemented it already?? I would like to do the same for image recognition but it seems like a very painful task.
Best regards.
Sergio Rubio has posted an implementation of using a vocabulary tree for image classification at http://sourceforge.net/projects/vocabularytree/. I had to rework much of the C code he posted to get it to work on my Windows system, but overall it was a very good resource for implementing the ideas presented in the original paper.
Recently I found a non-free pretty great Vocabulary Tree implementation in C++ called DBow.
The code is well organized and has a lot of comments.
Checkout here: http://webdiis.unizar.es/~dorian/index.php?p=31
and here: http://webdiis.unizar.es/~dorian/index.php?p=32
You want to look for a space-filling-curve or spatial index. A sfc reduce the 2d complexity to a 1d complexity although it is just an reorderd of the surface. A sfc recursivley subdivide the surface into smaller tiles and keep picking information of the near by tiles. It can be compared with a quadtree. This can be usefull to compare images because you compare near by tiles. The difficult is then to make the tiles comparable. I believe a DCT can be useful here. You want to look for Nick's hilbert curve quadtree spatial index blog.
I believe the Pyramid Match kernel method proposed by Grauman and Darrell is generally considered to be even better. You can get a C++ library implementation here.
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I'm doing research to implement automatic segmentation based on MRI (Magnetic Resonance Image) modals. For my case, prostate region is my focus . To make this happen, I'm thinking about these steps: 1. Image Acquisitions (about 20+ patients MRI - DICOM, each patient has around 15-30 slice images, and all these images will be dataset for training, you can see example of the dataset below)
From these dataset, I'm thinking to do manual segmentation with purpose to get the region of prostate (and the size of prostate in each slice is not consistent), so I can get the feature of prostate for any size, as you can see below. The green one is central of prostate, and the red one is peripheral zone of prostate.
So now, I have feature dataset of all slices, and I'm ready for train it to create classifier model.
?
As I'm still green of MATLAB (sorry for this), I have no idea to train the dataset to create classifier that can detect the region of prostate (in any size), and automatically give a boundary to it. Should I use classifier + segmentation algorithm (level set/active contour) to get this done or only using classifier algorithm can get this done?
I' learning about object detection algorithm such as; Haar-Like Feature, but can get all of this clear (yes, I'm screwed). I would be very grateful if anyone can help me to give a clear idea, and guide me to make this happen, please.
Very Thank you
I imagine that you could get good segmentation using superpixels (SLIC is a good approach for generating these), especially so since your images do not seem particularly complex. A common approach for building a classifier using superpixels is to train a CRF to learn the correct segmentation. This is a fairly common approach in computer vision and looks like it should do well given your data. Further, there are good implementations of both of these approaches in Matlab.
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I want to measure semantic similarity between two phrases/sentences. Is there any framework that I can use directly and reliably?
I have already checked out this question, but its pretty old and I couldn't find real helpful answer there. There was one link, but I found this unreliable.
e.g.:
I have a phrase: felt crushed
I have several choices: force inwards,pulverized, destroyed emotionally, reshaping etc.
I want to find the term/phrase with highest similarity to the first one.
The answer here is: destroyed emotionally.
The bigger picture is: I want to identify which frame from FrameNet matches to the given verb as per its usage in a sentence.
Update : I found this library very useful for measuring similarity between two words. Also the ConceptNet similarity mechanism is very good.
and this library for measuring semantic similarity between sentences
If anyone has any insights please share.
This is a very complicated problem.
The main technique that I can think of (before going into more complicated NLP processes) would be to apply cosine (or any other metric) similarity to each pair of phrases. Obviously this solution would be very inefficient at the moment due to the non-matching problem: The sentences might refer to the same concept with different words.
To solve this issue, you should transform the initial representation of each phrase with a more "conceptual" meaning. One option would be to extend each word with its synonyms (i.e. using WordNet, another option is to apply metrics such as distributional semantics DS (http://liawww.epfl.ch/Publications/Archive/Besanconetal2001.pdf) that extend the representation of each term with the more likely words to appear with it.
Example:
A representation of a document: {"car","race"} would be transform to {"car","automobile","race"} with synonyms. While, with DS it would be something like: {"car","wheel","road","pilot", ...}
Obviously this transformation won't be binary. Each term will have some associated weights.
I hope this helps.
Maybe the cortical.io API could help with your problem. The approach here is that every word is converted into a semantic fingerprint that characterizes the meaning of it with 16K semantic features. Phrases, sentences or longer texts are converted into fingerprints by ORing the word fingerprints together. After this conversion into a (numeric) binary vector representation semantic distance can easily be computed using distance measures like Euclidian Distance or cosine-similarity.
All necessary conversion- and comparison-functions are provided by the api.
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I have been learning alot about using graphs for machine learning by watching Christopher Bishops videos( http://videolectures.net/mlss04_bishop_gmvm/ ). I find it very interesting and watched a few others in the same categories(machine learning/graph) but was wondering if anyone had any recommendations for ways of learning more?
My problem is, although the videos gave a great high level understanding, I don't have much practical skills in it yet. I've read Bishops book on machine learning/patterns as well as Norvig's AI book but both don't seem to touch upon specific using graphs much. With the emergence of search engines and social networking, I would think machine learning on graphs would be popular.
If possible, can anyone suggestion an a resource to learn from? (I'm new to this field and development is a hobby for me, so I'm sorry in advance if there's a super obvious resource to learn from..I tried google and university sites).
Thanks in advance!
First, i would strongly recommend the book Social Network Analysis for Startups by Maksim Tsvetovat and Alexander Kouznetsov. A book like this is a godsend for programmers who need to quickly acquire a basic fluency in a specific discipline (in this case, graph theory) so that they can begin writing code to solve problems in this domain. Both authors are academically trained graph theoreticians but the intended audience of their book is programmers. Nearly all of the numerous examples presented in the book are in python using the networkx library.
Second, for the projects you have in mind, two kinds of libraries are very helpful if not indispensible:
graph analysis: e.g., the excellent networkx (python), or igraph
(python, R, et. al.) are two that i can recommend highly; and
graph rendering: the excellent graphViz, which can be used
stand-alone from the command line but more likely you will want to
use it as a library; there are graphViz bindings in all major
languages (e.g., for python there are at least three i know of,
though pygraphviz is my preference; for R there is rgraphviz which is
part of the bioconductor package suite). Rgraphviz has excellent documentation (see in particular the Vignette included with the Package).
It is very easy to install and begin experimenting with these libraries and in particular using them
to learn the essential graph theoretic lexicon and units of analysis
(e.g., degree sequence distribution, nodes traversal, graph
operators);
to distinguish critical nodes in a graph (e.g., degree centrality,
eigenvector centrality, assortivity); and
to identify prototype graph substructures (e.g., bipartite structure,
triangles, cycles, cliques, clusters, communities, and cores).
The value of using a graph-analysis library to quickly understand these essential elements of graph theory is that for the most part there is a 1:1 mapping between the concepts i just mentioned and functions in the (networkx or igraph) library.
So e.g., you can quickly generate two random graphs of equal size (node number), render and then view them, then easily calculate for instance the average degree sequence or betweenness centrality for both and observer first-hand how changes in the value of those parameters affects the structure of a graph.
W/r/t the combination of ML and Graph Theoretic techniques, here's my limited personal experience. I use ML in my day-to-day work and graph theory less often, but rarely together. This is just an empirical observation limited to my personal experience, so the fact that i haven't found a problem in which it has seemed natural to combine techniques in these two domains. Most often graph theoretic analysis is useful in ML's blind spot, which is the availability of a substantial amount of labeled training data--supervised ML techniques depend heavily on this.
One class of problems to illustrate this point is online fraud detection/prediction. It's almost never possible to gather data (e.g., sets of online transactions attributed to a particular user) that you can with reasonable certainty separate and label as "fraudulent account." If they were particularly clever and effective then you will mislabel as "legitimate" and for those accounts for which fraud was suspected, quite often the first-level diagnostics (e.g., additional id verification or an increased waiting period to cash-out) are often enough to cause them to cease further activity (which would allow for a definite classification). Finally, even if you somehow manage to gather a reasonably noise-free data set for training your ML algorithm, it will certainly be seriously unbalanced (i.e., much more "legitimate" than "fraud" data points); this problem can be managed with statistics pre-processing (resampling) and by algorithm tuning (weighting) but it's still a problem that will likely degrade the quality of your results.
So while i have never been able to successfully use ML techniques for these types of problems, in at least two instances, i have used graph theory with some success--in the most recent instance, by applying a model adapted from the project by a group at Carnegie Mellon initially directed to detection of online auction fraud on ebay.
MacArthur Genius Grant recipient and Stanford Professor Daphne Koller co-authored a definitive textbook on Bayesian networks entitled Probabalistic Graphical Models, which contains a rigorous introduction to graph theory as applied to AI. It may not exactly match what you're looking for, but in its field it is very highly regarded.
You can attend free online classes at Stanford for machine learning and artificial intelligence:
https://www.ai-class.com/
http://www.ml-class.org/
The classes are not simply focused on graph theory, but include a broader introduction in the field and they will give you a good idea of how and when you should apply which algorithm. I understand that you've read the introductory books on AI and ML, but I think that the online classes will provide you with a lot of exercises that you can try.
Although this is not an exact match to what you are looking for, textgraphs is a workshop that focuses on the link between graph theory and natural language processing. Here is a link. I believe the workshop also generated this book.
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What are in your opinion the best resources (books or web pages) describing algorithms or techniques to use for collision detection in a 2D environment?
I'm just eager to learn different techniques to make more sophisticated and efficient games.
Collision detection is often a two phase process. Some sort of "broad phase" algorithm for determinining if two objects even have a chance of overlapping (to try to avoid n^2 compares) followed by a "narrow phase" collision detection algorithm, which is based on the geometry requirements of your application.
Sweep and Prune is a well established efficient broad phase algorithm (with a handful of variants that may or may not suit your application) for objects undergoing relatively physical movement (things that move crazy fast or have vastly different sizes and bounding regions might make this unsuitable). The Bullet library has a 3d implementation for reference.
Narrow phase collision can often be as simple as "CircleIntersectCircle." Again the Bullet libraries have good reference implementations. In 3d land when more precise detection is required for arbitrary objects, GJK is among the current cream of the crop - nothing in my knowledge would prevent it from being adapted to 2d (but it might end up slower than just brute forcing all your edges ;)
Finally, after you have collision detection, you are often in need of some sort of collision response. Box 2d is a good starting point for a physical response solution.
Metanet Software has published some relevant tutorials. Metanet develops N (Flash-based, for Windows, Mac, Linux) and N+ (for the X360, DS, and PSP).
Personally, I love the work of Paul Bourke.
Also, Paul Nettle used to write on the topic. He has a full 3D collision detection library, but you may be more interested in the ideas behind such libraries (which are very applicable to 2D). For that, see General Collision Detection for Games Using Ellipsoids.
The book 'Real-Time Collision Detection' by Christer Ericson (ISBN: 1-55860-732-3) is a recent (2005) and widely praised book which should give you some good answers.
It starts with a basic primer of some of the maths you will need to know, and then goes into various types of bounding volumes (spheres, axis-aligned bounding boxes, oriented bounding boxes) commonly used in collision detection.
Next up for discussion are numerous algorithms for detecting collisions between various combinations of primitives, such as lines, triangles, spheres, polygons, planes, bounding volumes etc.
Also of importance is the coverage of some of the major methods of spatial division and organisation of your objects (volume hierarchies, BSP trees, Octrees, etc.). This essentially speeds up collision detection, as it allows you to subdivide your objects so you can avoid unnecessary comparisons between objects (e.g. I know from my data structures that object A is too far away to hit object B, so I won't even do a distance check).
It also includes some coverage of how to actually check for collisions between moving objects (intervals, etc) but be aware that even though this is a fairly hefty book and covers the material well, it is for collision detection, not resolution or response. So it will help you determine whether two objects have collided, but not really what to do about it, i.e. how to resolve it. The intersection tests will usually give you the data you need to make such decisions, but in terms of the general problem of writing a solver, which uses collision detection routines to detect collisions and then decide what to do about them, this book does not cover that in depth.
If your objects are represented as points in 2D space you can use line intersection to determine if two objects have collided. You can use similar logic to check if an object is inside another object (and thus they have collided even any of their lines are not currently intersecting). The math to do this is quite simple, and should be covered by any textbook on basic geometry. Detecting if an object has passed completely through an object might be a bit more tricky though.
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What are the most common problems that can be solved with both these data structures?
It would be good for me to have also recommendations on books that:
Implement the structures
Implement and explain the reasoning of the algorithms that use them
The first thing I think about when I read this question is: what types of things use graphs/trees? and then I think backwards to how I could use them.
For example, take two common uses of a tree:
The DOM
File systems
The DOM, and XML for that matter, resemble tree structures.
It makes sense, too. It makes sense because of how this data needs to be arranged. A file system, too. On a UNIX system there's a root node, and branching down below. When you mount a new device, you're attaching it onto the tree.
You should also be asking yourself: does the data fall into this type of structure? Create data structures that make sense to the problem and the rest will follow.
As far as being easier, I think thats relative. Are you good with recursive functions to traverse a tree/graph? What if you need to balance the tree?
Think about a program that solves a word search puzzle. You could map out all the letters of the word search into a graph and check surrounding nodes to see if that string is matching any of the words. But couldn't you just do the same with with a single array? All you really need to do is move an index to check letters to the left and right, and by the width to check above and below letters. Solving this problem with a graph isn't difficult, but it can create a lot of extra work and difficulty if you're not comfortable with using them - of course that shouldn't discourage you from doing it, especially if you are learning about them.
I hope that helps you think about these structures. As for a book recommendation, I'd have to go with Introduction to Algorithms.
Circuit diagrams.
Compilation (Directed Acyclic graphs)
Maps. Very compact as graphs.
Network flow problems.
Decision trees for expert systems (sic)
Fishbone diagrams for fault finding, process improvment, safety analysis. For bonus points, implement your error recovery code as objects that are the fishbone diagram.
Just about every problem can be re-written in terms of graph theory. I'm not kidding, look at any book on NP complete problems, there are some pretty wacky problems that get turned into graph theory because we have good tools for working with graphs...
The Algorithm Design Manual contains some interesting case studies with creative use of graphs. Despite its name, the book is very readable and even entertaining at times.
There's a course for such things at my university: CSE 326. I didn't think the book was too useful, but the projects are fun and teach you a fair bit about implementing some of the simpler structures.
As for examples, one of the most common problems (by number of people using it) that's solved with trees is that of cell phone text entry. You can use trees, not necessarily binary, to represent the space of possible words that can come out of any given list of numbers that a user punches in very quickly.
Algorithms for Java: Part 5 by Robert Sedgewick is all about graph algorithms and datastructures. This would be a good first book to work through if you want to implement some graph algorithms.
Scene graphs for drawing graphics in games and multimedia applications heavily use trees and graphs. Nodes represents objects to be rendered, transformations, controls, groups, ...
Scene graphs usually have multiple layers and attributes which mean that you can draw only some node of a graph (attributes) in a specified order (layers). Depending on the kind of scene graph you have it can have two parralel structures: declarations and instantiation. Th
#DavidJoiner / all:
FWIW: A new version of the Algorithm Design Manual is due out any day now.
The entire course that he Prof Skiena developed this book for is also available on the web:
http://www.cs.sunysb.edu/~algorith/video-lectures/2007-1.html
Trees are used a lot more in functional programming languages because of their recursive nature.
Also, graphs and trees are a good way to model a lot of AI problems.
Games often use graphs to facilitate finding paths across the game world. The graph representation of the world can have algorithms such as breadth-first search or A* in order to find a route across it.
They also often use trees to represent entities within the world. If you have thousands of entities and need to find one at a certain position then iterating linearly through a list can be inefficient, especially if you need to do it often. Therefore the area can be subdivided into a tree to allow it to be searched more quickly. Just as a linear space can be efficiently searched with a binary search (and thus divided into a binary tree), 2D space can be divided into a quadtree and 3D space into an octree.