my aim is towfold:
-computing the similarity measure , through neural network , from the relevant and non relvant skin lesion image database.
-use this measure to retrieve set of images.
so, how to learn this measure and how to use it in retrieval?
You seriously need to concretize your question otherwise it is impossible to help.
What exactly is your goal? Distinguish different kinds of lesions? Distinguish between wounded and not wounded? Distinguish the lesions locations?
What kind of similarity measure do you use?
What features do you use? Are they implemented and ready?
...
Hello Robert,
my aim is to differentiate between eczema and pseuriasis(diagnosis).
i must select the most discriminative features between eczema and pseuriasis.
how i do that using neural network ? i.e how i weight of features .
and i would like to retieve the similair images for eczema or pseuriasis based on weighted similarity obtained from the classification.
but the question, how to compute weighted similaritymeasure? can you explailn me please?
Thank you Robert
What would a human do?
How do you differentiate between eczema and pseuriasis(diagnosis)?
Color? Shape? Intensity? Texture? What are the discriminating features?
How does your data look like? Intensity data? Color data?
A lot different approaches can be comprehended as 'weighted similaritymeasure', this phrase is not an individual name for one unique technique ...
In short, what you need is a standard pattern recognition chain: 1) image acquisition 2) image preprocessing 3) feature extraction and 4) classification. As far as I understood, you do not acquire the data yourself, so you will have to start at 2).
hello Robert Fischer,
How do you differentiate between eczema and pseuriasis(diagnosis)?
Color? Shape? Intensity? Texture? What are the discriminating features?
-I compute the dermoscopic structure .in addition to the color for both images .
the discriminating features is the features that are uncommon for eczema and pseuriasis images.for example in eczema ,there are vesicles in contrast to pseuriasis.
and to select that (dermoscopic structure) i think to use feature selection techniques based on neural network, which allow to select the most discriminating features that differentiate between eczema et pseuriasis.
but i don't have any idea how i weight the extracted features? what i must do?
also, i would like to retireve the similair images for both images that's why i must compute the " weighted similarity measure" resulted from the classafication but i don't know how ?
Thank you Robert Fischer.
I am not sure if understand you correctly, but I am afraid you are confusing some things.
A similaritry measure is not the outcome of the classification. The result of your classifier is a class assignment for each instance / sample. A similarity measure can be used for this class assignment. If I understand you correct you got a typical 2-class classification scenario, you got a set of samples and you want to decide if the samples either belong to class A or class B.
In a nutshell:
- calculate the features for each of your samples
- this results in a n-dimensional feature vector as a representation of each sample in feature space
- find the features which are most discriminative and weight them higher
- find a rule (e.g. a similarity measure) to distinguish between class A and B
There are lots approaches for similarity measures. A very simple example would be: if you have two feature vectors you can compare them by calculating the euclidean distance (or any other distance measure) between them.
There are lots approaches for weighting features, a very simple example would be to use weighting factors. For example, if you have two features 'a' and 'b' and 'b' is more discriminative than 'a', you could use a weight of 0.5 for feature 'a' and 2.0 for feature 'b' when making the decission. This would cause feature 'b' to have 4-times the impact on the classification decission compared to feature 'a'.
A small and very simple example:
feature vector 1: {3, 2}
feature vector 2: {1, 5}
euclidean distance between 1 and 2: (3-1)^2 + (2-5)^2 = 4+9 = 13
distance = 13
now we weight feature 'a' with 0.5 and 'b' with 2.0: 0.5((3-1)^2) + 2.0((2-5)^2) = 2+18 = 20
distance = 20
I hope that helps a bit ...
Hello Khadidja,
Please go through this this may helpful to you,
Features are extracted for each image in the database as well as for the query image. Feature selection method is used for selecting the optimal features. Relevant images are retrieved from the database using different similarity measures. The efficiency of the retrieval system is evaluated using the two performance measures, precision and recall and also comparison of different similarity metrics has been done.
Feature selection algorithm is a computational solution to pinpoint relevant features. The feature selection method is to upsurge the precision of similarity searches and significantly decrease the data dimensionality. FS scale down the number of features by removing irrelevant, redundant, and noisy data. The optimal feature is selected using Genetic Algorithm that searches for the best feature subset corresponding to evaluation criteria positioned on the ranking quality.
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