The overall objective is to cluster the near-duplicate images. Initially, the user passes the query to the search engine and the search engine results in set of query related images. These images contain duplicate as well as near-duplicate images. The main aim of this paper is to detect near-duplicate images and cluster those images. This is achieved through the following steps – Image Preprocessing, Feature Extraction and Clustering. In image processing, the initial step is preprocessing. Image preprocessing is nothing but noise removal and image enhancement. Then feature extraction includes the extraction of key points and key points matching. These matched key points are allowed for estimation of affine transform based on an affine invariant ratio of normalized lengths. At last, Clustering is performed which includes Supervised and Unsupervised Clustering. This results in cluster of images. Each of these clusters will have one image as a representative of that cluster and other images in the cluster is called its near-duplicates. At last, performance measure is calculated for the evaluation of algorithm accuracy. Figure 1 shows the block diagram of the proposed system. It is seen that the final output will be many clusters; each consisting of near-duplicates relating to the representative cluster. Fig. 1. Block Diagram of the Proposed System 3.1 Image Preprocessing Pre-processing methods use a small neighborhood of a pixel in an input image to get a new brightness value in the output image; also called filtration. Local pre-processing methods can be divided into the two groups according to the goal of the processing: Smoothing suppresses noise or other small fluctuations in the image; equivalent to the suppression of high... ... middle of paper ... ...o cut. The brief idea is clustering is done around half data through Hierarchical clustering and succeed by K-means for the remaining. In order to create super-rules, Hierarchical is terminated when it generates the largest number of clusters. Algorithm – 1. Finish a complete agglomerative Hierarchical clustering on the data and record number of clusters generated during the process. 2. Run the agglomerative Hierarchical clustering again and stop the process when largest number of clusters is generated. 3. Execute the k-means clustering on the remaining data which are not processed in the step 2 and use the centroids for every cluster in step 2 and are served as initial centroids in the k-means clustering algorithm. After the clustering process is over, set of clusters will be found. Each cluster represents a set of near-duplicates with one representative image.
The K-Means algorithm is used for cluster analysis by dividing data points into k clusters. The K means algorithm will group the data into the cluster based on feature similarity.
Clustering This is un-supervised learning method. Text documents here are unlabelled and inherent patterns in text are revealed through cluster formation. This can also be used as prior step for other text mining methods.
The first sub phase of this is prophase 1 and this is split up into 5 stages. The first one is leptotene and this is where the chromosomes supercoil. The second one is zygotene and this is where the homologous chromosomes form pairs and these are called bivalents. Pachytene is where crossing over occurs between the homologous chromosomes and chiasmata form. Diplotene is where they start to separate but remain attached to each other by the chiasmata. Diakinesis is the last stage and this is where they keep separating and the chiasmata moves to the ends of the chromosome (GENIE, 2010). The second phase is prometaphase and this is where the spindle fibres assemble and the chromosomes become anchored to them by their kinetochores. Metaphase 1 is where the bivalents assort randomly on the metaphase plate. This helps to create genetic diversity. Anaphase 1 is where the bivalents separate and the homologous chromosomes move to opposite poles of the cell. Telophase 1 is where the nuclear envelope reforms after disintegrating in prophase 1. Then cytokinesis is where the cell divides to create two new cells which are haploid (GENIE, 2010). The next main stage is meiosis 2 and this is where each chromosome is split into 2 sister chromatids. Prophase 2 is the first stage and this is where the chromosomes supercoil, the mitotic spindle forms and the nuclear envelope disintegrates. Metaphase 2 is where the chromosomes become attached to the
35 Meng Xiaofeng and Ci Xiang, 2013 : Big Data Management: Concepts,Techniques and Challenges pp 4-6
*B - (1) At that point the histogram levelling which appropriates the forces of the pictures is connected on the picture to improve the plant ailment pictures. The combined circulation work is utilized to disperse power esteems. C] Image Segmentation: Division implies dividing of picture into different piece of same highlights or having some likeness. The division should be possible utilizing different techniques like Otsu' strategy, k-implies grouping, changing over RGB picture into HIS model and so on.
Let us see now how this algorithm works. The algorithms randomly creates solutions. Each one of these solutions has a fitness value based on some criteria. Those solutions of a specific problem are also called Phenotype, while the encoding of each solution is called Genotype. We refer on Representation as the procedure of establish the mapping between genotypes and phenotypes. Representation is used as in two different ways. As mentioned before, representation establish the mapping between the genotype and the phenotype. This means that representation could encode ore decode the candidate solutions.
In this paper, the authors define ground truth communities by selecting networks where the nodes define their relationship with the groups. After determining the ground truth communities, a comparison is performed between the network communities and the ground truth communities to find out difference of result in 13 chosen structural definitions of network communities. These 13 structural definitions gets partitioned into four classes and tested on the basis of three parameters: sensitivity, reliability and performance to determine the ground truth. Besides this, author also tries to find the network communities in case of a single node. To achieve the task author applies spectral clustering along with heuristic parameter-free algorithm to detect the communities of the node. The advantage of this algorithm is that it is extremely scalable and can be applied to networks with millions of nodes. We have studied clustering in our lecture; here the clusters might overlap as a node can have relationships with many communities, it is not confined to only one community, it means that the nodes are not exclusive. Also, these nodes in the network form densely linked clusters.
Centrosome biogenesis is initiated in the G1 phase. The centrosome duplication cycle which is normally coordinated with the cell cycle, also faces exceptions in the case of spermatogenesis in which centrosome duplication occurs before the second meosis, and in cancer cell.
Gaussian filter is exclusively used for this purpose as the mask is simple. The standard convolution method is performed once the mask is calculated. Since the convolution mask is usually much smaller than the actual image, the mask slides over the image , manipulating the pixels in the image. The large width Gaussian masks are not preferred as detector's sensitivity to noise is low and moreover, the localization error in the detected edges also increases with increase in Gaussian mask width.
Local adaptive thresholding, on the other hand, selects an individual threshold for each pixel based on the range of intensity values in its local neighbourhood. This allows for thresholding of an image whose global intensity histogram doesn't contain distinctive peaks.
The final step is called rendering. During rendering, the computer calculates the effect of light, color, and texture on the model's surface. For a film or video, the computer will produce a two-dimensional digital picture of the characters for each frame of the animation. The computer artist usually adjusts many visual effects, such as camera focus and transparency, during the rendering phase.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
It is used by optical engineers and scientists to describe how the optics project light from the object or scene onto a photographic film, detector array, retina, screen or simply the next item in the transmission chain. The function specifies the translation and contrast reduction of a periodic sine pattern after passing through the lens system, as a function of its periodicity and orientation. Formally, the optical transfer function is defined as the Fourier transform of the point spread function, or impulse response of the optics, i.e. the image of a point source. When this image does not change shape upon lateral translation of the point source, the optical transfer function can be used to study the projection of arbitrary objects or scenes onto the detector or film. While figures of merit such as contrast, sensitivity, and resolution give an intuitive indication of performance, the optical transfer function provides a comprehensive and well-defined characterization of optical
Generates a population of points for each iteration, leading to multiple options for solution out of which the best is to be selected.
By searching correct feature point and setting bidirectional threshold value,the matching process can be quickly and precisely implemented with optimistic result. The resemblance of two images is defined as the overall similarity between two families of image features[1]. Same proportion image matching algorithm using bi-directional threshold image matching technique is used. Small window of pixels in a reference image (template) is compared with equally sized windows of pixels in other (target) images. In FBM, instead of matching all pixels in an image, only selected points with certain features are to be matched. Area based matching provide low speed. feature based matching algorithm is faster in comparison to the area based matching technique. feature based matching time complexity depend on number of feature to be selected as well as right or wrong threshold. If the number of feature are high then sometimes it takes more computational time in comparison to area based feature. The number of features extracted from an image depends largely on the contents of an image. If there are high variations then features computed are high. This reduces time efficiency to