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The use of multimedia data such as images, audio and video etc in daily life led to a huge amount of images in fairly large image databases. Nowadays, the image becomes a focal source of information because it hides a precious knowledge. In Image Mining, the aim is to discover this knowledge and gives the relevant information or patterns that are presented. This paper is dedicated to a review on image...
This paper presents a data analytics approach for recovering test-pad information from images of printed circuit boards. The main aim is to obtain highly accurate information as input to a robotic flying probe tester. Such a tester is a mechatronic system that is able to perform a great variety of diagnostic testing on printed circuit boards without any additional circuit board documentation. In this...
This paper discusses the relation between dorm arrangement and student performance. One of the unsupervised learning algorithms, k-means algorithm, is mainly used in the process of analysis. Students are clustered into several clusters according to their similarity of performance scores. This paper analyzes the result of clustering by comparing it with actual dorm arrangement. In the end, drawbacks...
Now a days the work on Sparse representation of signals has emerged as a major research part. It is well-known that the many natural signals such as image, music and video signals are represented sparsely if decomposed by using a proper choosen dictionaries for e.g. formed of wavelets bases. Sparse representation and compression distance is the representation that account for most or all the information...
The video key frame extraction technology is one of the important parts of content-based video retrieval. And the mainstream of key frame extraction is the algorithm based on clustering. The basic idea is: the video frames are grouped in accordance with the correlation of the visual content by clustering, and then we extract the most representative frame from each group as a key frame. In this paper,...
Mobile Phone based Participatory Sensing (MPPS) systems involve a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. We have developed the tourist subjective data collection system with Android smartphone. The tourist can tweet...
In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing...
Data mining techniques are being applied successfully in wide varieties of databases in order to extract useful information. This paper applies data mining techniques on a new tea insect pests database created on the basis of data available from different tea gardens of North Bengal districts of India. We describe different issues related to the development of a good data mining model in the present...
The goal of this work is to automatically detect frequently occurring groups of media in a user's collection that have a unifying theme. These groups provide a narrative structure that ties in images that are temporally far apart and cannot be browsed easily. The media in the collection is analyzed by a variety of algorithms to generate metadata of different types. The media and associated metadata...
Key frame extraction methods aim to obtain a set of frames that can efficiently represent and summarize video contents and be reused in many video retrieval-related applications. An effective set of key frames, viewed as a high-quality summary of the video, should include the major objects and events of the video, and contain little redundancy and overlapped content. In this paper, a new key frame...
Photo-sharing websites such as Flickr and Panoramio contain millions of geotagged images contributed by people from all over the world. Characteristics of these data pose new challenges in the domain of spatio-temporal analysis. In this paper, we define several different tasks related to analysis of attractive places, points of interest and comparison of behavioral patterns of different user communities...
With the development of the technique of DNA chips, more and more experiments data can be gained. Genes exhibiting similar patterns are often functionally related, which is very helpful for the research on analyzing the underlying mechanisms of metabolic and regulatory networks in the cell. But the fact that the correlated part is entangled with the unrelated part in the data, and the data is usually...
This work presents an application of TurSOM for high-dimensional segmentation and cluster identification. TurSOM is a variation algorithm of the SOM algorithm, which introduces a new mechanism of self-organization: connection reorganization. We have theoretically presented TurSOM in very recent previous work, however, the applicability of the novel architecture is expanding as we explore it numerous...
With the advancement in image capturing device, the image data been generated at high volume. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images...
We propose an image annotation approach that relies on fuzzy clustering and feature discrimination, a greedy selection and joining algorithm (GSJ), and Bayes rule. Clustering is used to group image regions into prototypical region clusters that summarize the training data and can be used as the basis of annotating new test images. Since this problem involves clustering sparse and high dimensional...
The paper presents an evaluation of four clustering algorithms: k-means, average linkage, complete linkage, and Wardpsilas method, with the latter three being different hierarchical methods. The quality of the clusters created by the algorithms was measured in terms of cluster cohesiveness and semantic cohesiveness, and both quantitative and predicate-based similarity criteria were considered.Two...
After the analysis of the problems a raster-to-vector (R2V) software can meet, a strategy involving a pre-processing, a clustering, a labeling and finally a vectorization phase is proposed. Much emphasis is put on the clustering and the labeling phases which depend on the pixel type (edge, line, or other). In particular, it is suggested to use the median-shift on all the pixels but the edgels to extract...
Clustering algorithms are the core technique of data mining, machine learning, pattern matching, bioinformatics and a number of other fields. This paper proposes a new clustering method based on attribute partitioning and a novel data visualization method. In a nutshell, the idea for our method is based on two steps: 1) cluster data set using primary and secondary attributes of data; 2) map color...
In this paper, we propose a novel framework for automated analysis of surveillance videos. By analysis, we imply summarizing and mining of the information in the video for learning usual patterns and discovering unusual ones. We approach this video analysis problem by acknowledging that a video contains information at multiple levels and in multiple attributes. Each such component and co-occurrences...
The main goal of colour quantization methods is a colour reduction with minimum colour error. In this paper were investigated six following colour quantization techniques: the classical median cut, improved median cut, clustering k-means technique in two colour versions (RGB, CIELAB) and also two versions of relative novel technique named k-harmonic means. The comparison presented here was based on...
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