International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 4 - Mar 2014
C.Vijaya Lakshmi
1
, Ms.Refonaa
2
1
Post graduate student, SathyabamaUniversity,Chennai,India.
2
Faculty, SathyabamaUniversity,Chennai,India.
Abstract
Pattern Recognition is assigning labels to the given input values. The basic procedures to recognize a pattern is to pre-process the given data which is the initial step, extract the features, and then classify. In the preprocessing step, we adjust the light levels and threshold the image and hence remove the unwanted background. We use the extraction method SIFT (Scale Invariant Feature
Transform) to detect the local features in the given training set of images. We generate key points of the query image and match every image from the training set with the key points of the query image using the Similarity Analysis.
Keywords – Clustering, Pattern Recognition, SIFT three types namely spatial patterns, temporal patterns and abstract patterns.
To apply pattern recognition to an image, it should be split into segments. This process has two steps Segmentation and Pattern Recognition. In any image analysis, preprocessing is the initial stage. During this stage, we try to increase the brightness or the intensity of the pixels of the required objects from the image and hence we concentrate more on the pattern in our image. We also eliminate the noise in the image using the image smoothing techniques. We use position dependence and gray scale transformations for brightness correction in the required image during the initial stages of image processing.
II.
RELATED WORK I.
I
NTRODUCTION
Clustering is the basic of data analysis methods such as classification which is the main technique in image segmentation. A good clustering gives the compressed representation of the query image’s overall density and structure. The number of clusters, which is represented as a constant k, is itself the parameter in most of the clustering techniques.
Clusters can be both isotropic and anisotropic in a multidimensional feature. In isotropic clustering, the feature space of the image is assumed to be equal in all sides, whereas the shape of the data is not considered so. In anisotropic clustering the query image’s direction and distance are taken into account while calculating it’s feature space.
Almost many isotropic clustering algorithms rely on initial state or the number of clusters should be known to start with.
But to perform anisotropic clustering, the number of clusters to be formed need not be known at the initial stage
Pattern recognition is the detection and extraction of patterns from the given data.Pattern recognition employs clustering in an efficient manner. In simple words pattern recognition can be described as finding the required pattern from the given set of inputs, using the computer. The inputs may be some text, audio, video or images etc. the proper definition can be given as “Pattern Recognition is the study of algorithms which gives the computer the ability to classify the training set of data into similar patterns in a reliable manner”.
Pattern recognition is the core part of the Artificial
Intelligence which enable the computers to do some tasks, and whose output is more reliable and faster than performed by humans. Generally patterns can be broadly classified into
In [1], they have defined non-spherical clusters of different sizes by using the properties of the Gaussian kernel.
Although they have used Gaussian properties, they have avoided the problem of singularities. In this method the number of clusters need not be known initially. Here, clusters are found one at a time. The main idea here is the kernel is first initialized and fitted to weighted Gaussian data. The weight and shape of the Gaussian data is updated in the kernel iteratively until it converges. Once the kernel converges, its data is removed and its parameters are stored. This process is continued until all data is processed and hence we get the clusters finally.
In [2], they have developed pattern recognition algorithm using Radial Basis Function neural network to implement real time face tracking and recognition. The Radial Basis
Function neural network is a member of kernels classifiers which uses simple functions which overlap each other to cover complex regions.
In [3], documents are clustered effectively using extensive similarity measures and the comparisons between the query data and the training sets of data are made using fmeasure and normalized mutual information. The extensive similarity method used in this paper to cluster the related documents is symmetric and it satisfies the triangular inequality.
In [4], the quality of the image is measured. The image is first pre-processed and its local variants are detected. Then standard metrics are applied to measure the visual qualities of the reference image and the pre-processed image. In
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 4 - Mar 2014 this paper, totally three types of full reference metrics are applied to measure the image quality, and they are PSNR,
MSSIM and VSF. points to represents the shape it takes in the feature space in a better manner. The Gaussian kernel is used here to find nonspherical clusters of different sizes. Finally, the singularities are eliminated using the heuristic methods.
In [5], template matching is performed efficiently in images. To achieve this integral images are calculated for both the query image and the template images. The algorithm used for this purpose is the FFT based template matching method.
IV.
PROPOSED SYSTEM
In [6], the high dimensional feature space is easily formulated in kernels using sparsely based detection (SD) algorithm. The kernel sparse representation based density algorithm (KSD) is actually an extension of SD algorithm.
The primary step which is the dimensionality reduction is achieved using PCA.
In [7], the multi spectral space image is classified using
Concurrent Self-Organizing Maps (CSOM), which is one of the latest artificial computer intelligence models.
CSOM model needs less training time when compared to its earlier counterparts.
In [8], the local invariant features for retrieving overhead images are described. To do this they have evaluated the local invariant features for retrieving the images of land use/ land cover classes in high resolution aerial imagery.
They also compare some standard features like color and texture.
III.
EXISTING SYSTEM
The most commonly used algorithm for clustering is kmeans. This repeatedly alternates between the assignments of data to their closest cluster centre and shifting every cluster kernel to the centroid of its related cluster. Here the parameters are required, i.e. the number of clusters to be formed must be known initially. One of the initialization methods for this algorithm is selecting the random data seeds.
Generally k-means assumes that clusters are spherical or isotropic. But there are cases where the clusters are ellipsoidal.
The standard of the cluster a lot in selecting the initial parameters.
The other standard alternative for k-means clustering algorithm for pattern recognition is the mean shift algorithm.
Mean shift algorithm can be used in many areas like segmenting the color images and categorizing the objects.
Mean shift algorithm is non- parametric in nature. It has the ability to find clusters of different shapes and sizes.
Unfortunately, not clusters of all shapes can be found using this algorithm.
In the proposed system, the local features are analysed using
SIFT algorithm. Local features are small square images taken from the original images. Local features can yield good results in classification.
Local features have some interesting properties for image recognition, e.g. they are inherently robust against translation.
From one image we can extract many local features. Although the number of local features extracted from a single image may differ, generally it lies between 100 and 1000. The two important steps in the feature extraction process are the training step and the testing step. During the training step, local features are extracted from all the training set of data and these results in large number of local features with multidimensions. During the testing step the KD tree is created to enhance the faster searching among these local features. And also PCA algorithm is used to reduce the dimensions.
SIMILARITY ANALYSIS:
This module contains two steps, first we obtain the symbolic representations of both images and queries and then construct an edge list corresponding to each of the symbolic image and the query image. Finally, similarity analysis is applied to find out the similarities or closeness of the two edges. The algorithm to find out the similarity analysis is as follows :
Algorithm SA
Similarity ←0 .0
n ← number of objects in the query image
← edge list of the query image
← edge list of the database image
For each edge ei ∈ , find the corresponding edge ej ∈
If the corresponding edge is found, calculate angle θ between ei and ej
Then the quick unsupervised anisotropic algorithm is developed to find clusters efficiently in a multidimensional data. Here, each and every data point has been replaced with the kernel density of the point. Quac method helps us to alternate the kernel and hence we can aggregate the data
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 4 - Mar 2014
The
are descripted as
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V.
CONCLUSION
In this paper, patterns are recognized effectively in the overhead images. The key points in the query image are matched perfectly with the key points of the traning set of images. To do this we employ similarity algorithm. By this we get the images which matches exactly with the query image.
This paper hence suggests the best method to perform clustering for pattern recognition in an efficient manner in multidimensional data.
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