Appling parallelism in image mining

Image mining deals with the study and development of new technologies that allow accomplishing this subject. A common mistake about image mining is identifying its scopes and limitations. Clearly it is different from computer vision and image processing areas. Image mining deals with the extraction of image patterns from a large collection of images, whereas the focus of computer vision and image processing is in understanding and/or extracting specific features from a single image. On the other hand it might be thought that it is much related to content-based retrieval area, since both deals with large image collections. Nevertheless, image mining goes beyond the simple fact of recovering relevant images, the goal is the discovery of image patterns that are significant in a given collection of images. As a result, an image mining systems implies lots of tasks to be done in a regular time. Images provide a natural source of parallelism; so the use of parallelism in every or some mining tasks might be a good option to reduce the cost and overhead of the whole image mining process. At this work we will try to draw the image minnig problem: its computational cost, and to propose a possible global or local parallel solution.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI

Implementasi Teknik Data Mining untuk Sistem Pemindai Barang di Bandara

Saat ini, bandara menjadi salah satu tempat yang sangat vital dan harus memiliki tingkat keamanan yang sangat tinggi Permasalahan utama yang timbul ialah bagaimana mengoptimalakan sistem pengamanan bandara yang telah ada sehingga mampu meningkatkan keamanan dan kenyamanan calon penumpang pesawat. Pada paper ini kami menyajikan sebuah ide untuk meningkatkan keamanan bandara dengan penerapan image mining pada mesin pindai sebagai pemberitahuan awal apabila terdapat barang yang mencurigakan. Dalam tulisan ini, kami melakukan beberapa tahapan terhadap database citra dari hasil pindai antara lain melakukan ekstraksi pada fitur untuk mengetahui pola dari barang berbahaya yang ada. Pola tadi akan dimasukkan kedalam lookup table berdasarkan kriteria atau tingkatan yang diinginkan

Image Analysis Using ImageTool to Detect Dangerous Object in Airport Baggage Scanner

Airport is one of the vital places that need very extra security level. The major problem is how to optimize current airport security system to increase security and flight passenger’s comfort. In this paper we suggest an idea to increase airport security by implementation of image mining in scanner machine as an early warning if there are dangerous object

An Intelligent Multi-Resolutional and Rotational Invariant Texture Descriptor for Image Retrieval Systems

To find out the identical or comparable images from the large rotated databases with higher retrieval accuracy and lesser time is the challenging task in Content based Image Retrieval systems (CBIR). Considering this problem, an intelligent and efficient technique is proposed for texture based images. In this method, firstly a new joint feature vector is created which inherits the properties of Local binary pattern (LBP) which has steadiness regarding changes in illumination and rotation and discrete wavelet transform (DWT) which is multi-resolutional and multi-oriented along with higher directionality. Secondly, after the creation of hybrid feature vector, to increase the accuracy of the system, classifiers are employed on the combination of LBP and DWT. The performance of two machine learning classifiers is proposed here which are Support Vector Machine (SVM) and Extreme learning machine (ELM). Both proposed methods P1 (LBP+DWT+SVM) and P2 (LBP+DWT+ELM) are tested on rotated Brodatz dataset consisting of 1456 texture images and MIT VisTex dataset of 640 images. In both experiments the results of both the proposed methods are much better than simple combination of DWT +LBP and much other state of art methods in terms of precision and accuracy when different number of images is retrieved.  But the results obtained by ELM algorithm shows some more improvement than SVM. Such as when top 25 images are retrieved then in case of Brodatz database the precision is up to 94% and for MIT VisTex database its value is up to 96% with ELM classifier which is very much superior to other existing texture retrieval methods

Appling parallelism in image mining

Image mining deals with the study and development of new technologies that allow accomplishing this subject. A common mistake about image mining is identifying its scopes and limitations. Clearly it is different from computer vision and image processing areas. Image mining deals with the extraction of image patterns from a large collection of images, whereas the focus of computer vision and image processing is in understanding and/or extracting specific features from a single image. On the other hand it might be thought that it is much related to content-based retrieval area, since both deals with large image collections. Nevertheless, image mining goes beyond the simple fact of recovering relevant images, the goal is the discovery of image patterns that are significant in a given collection of images. As a result, an image mining systems implies lots of tasks to be done in a regular time. Images provide a natural source of parallelism; so the use of parallelism in every or some mining tasks might be a good option to reduce the cost and overhead of the whole image mining process. At this work we will try to draw the image minnig problem: its computational cost, and to propose a possible global or local parallel solution.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI

Image Analysis and Image Mining Techniques: A Review

This paper presents the analysis of existing literature which is relevant to mining and the mechanisms associated with weighted substructure. Though, the literature consists of a lot many research contributions, but, here, we have analysed around thirty-five research and review papers. The existing approaches are categorized based on the basic concepts involved in the mechanisms. The emphasis is on the concept used by the concerned authors, the database used for experimentations and the performance evaluation parameters. Their claims are also highlighted. Our findings from the exhaustive literature review are mentioned along with the identified problems. This paper is useful for comparative study of various approaches which is prerequisite for solving image mining problem

A New Feature Selection Method Based on Class Association Rule

Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may be impractical for multiple reasons such as the experience required in the application domain, care, accuracy, and time. In this research, we propose a new hybrid Filtering method called Class Association Rule Filter (CARF) that deals with the aforementioned issues by identifying relevant features through the Class Association Rule Mining approach and then using these rules to define weights for the available features in the training dataset. More crucially, we propose a new procedure based on mutual information within the CARF method which suggests the subset of features to be retained by the end-user, hence reducing time and effort. Empirical evaluation using small, medium, and large datasets that belong to various dissimilar domains reveals that CARF was able to reduce the dimensionality of the search space when contrasted with other common Filtering methods. More importantly, the classification models devised by the different machine learning algorithms against the subsets of features selected by CARF were highly competitive in terms of various performance measures. These results indeed reflect the quality of the subsets of features selected by CARF and show the impact of the new cut-off procedure proposed