2014-12-01 23:45:11 -0600 | commented question | What is stored in descriptor.yml file thanks @berak. Yes my file was broken. Its a trained Vocabulary file, where each column represents a feature of that small image in that ( i-th ) dimension. |
2014-12-01 23:38:14 -0600 | received badge | ● Scholar (source) |
2014-12-01 23:37:18 -0600 | commented question | how to store bag of words vocabulary efficiently? Please check this link http://pascallin.ecs.soton.ac.uk/challenges/VOC/ |
2014-12-01 23:36:21 -0600 | asked a question | On what factors does "Bag of Words" training efficiency depends? Hello all, Is it better to train a classifier on "Low Res" images than "High res" images? I am asking this question to efficiently classify objects based on their shape. Such that It will search in the vocabulary of that particular shape other than matching it against the whole homograph(~60MB). I am working on a project to identify products in a Supermart.
My idea is to classify products based on shape like- Please guide me if I am going in wrong direction. Thanks for your help. Aditi K |
2014-12-01 05:58:04 -0600 | received badge | ● Student (source) |
2014-12-01 03:09:34 -0600 | asked a question | how to store bag of words vocabulary efficiently? Hello All, I am training my system against Partially Annotated Databases The Caltech Database given here http://pascallin.ecs.soton.ac.uk/challenges/VOC/databases.html. I am using "bag of words" algorithm for vocabulary building and detection. My YML file is huge. So everytime I pass an image for detection the ~65MB yml file is loaded and then the object is detected. How can I do it more efficiently ?? Or is there some other way to store the vocabulary ?? I am badly stuck at it please help!! Thanks in advance. Aditi K |
2014-12-01 01:07:26 -0600 | asked a question | What is stored in descriptor.yml file Hi everyone, I am very new to Opencv. I want to know what is stored in "Descriptor.yml" file. I am using this project https://github.com/royshil/FoodcamClassifier. I am trying to understand this project. I want to know what all these numbers mean.. %YAML:1.0 training_descriptors: !!opencv-matrix rows: 17074 cols: 192 dt: f data: [ -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., -431602080., and so on... ] I am really confused. I am trying to understand how it works. Any hint will be a great help. Thanks in advance. Aditi K |
2014-11-27 00:42:30 -0600 | asked a question | how to train image database save and retrieve results faster? I have just started with OpenCV. My motivation is object detection. I am using Bag of words algorithm http://docs.opencv.org/trunk/modules/... I am using caltech database. Partially Annotated Databases The Caltech Database http://pascallin.ecs.soton.ac.uk/chal... I have attached my working code. So far the recognition is based on MAT() which is formed after looping over all the positive dataset images. So everytime I introduce a new test image out of the training dataset the code build the new MAT(), bowTrainer.add(features), and new Vocabulary. I want to remove this step of training again and again. So that when i give a new image it checks against "final trained matrix" and gives result quickly. Also, how to implement it such a way that after predicting the class of input image system is trained afterwards?? (more) |