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Traditional iris recognition systems can achieve excellent performance in both verification and identification. However, most of the existing systems adopted a similar technique to deal with the iris image. In this paper, we propose a novel matching strategy with invariant properties, which is based on the possibilistic fuzzy clustering algorithm, to compare a pair of local feature sets. Moreover,...
Automatic facial image analysis has been a long standing research problem in computer vision. A key component in facial image analysis, largely conditioning the success of subsequent algorithms (e.g. facial expression recognition), is to define a vocabulary of possible dynamic facial events. To date, that vocabulary has come from the anatomically-based Facial Action Coding System (FACS) or more subjective...
In today's world, where terrorist attacks are on the rise, employment of infallible security systems is a must. Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes Iris recognition systems unavoidable in emerging security & authentication mechanisms. We propose an iris recognition system based on vector quantization. The proposed system does...
The scale invariant feature transform (SIFT) has been used widely as a tool in object recognition. However, when there are several keyframes for one object in the training database, the number of keypoint descriptors for that object might be huge. The matching process of a test keypoint has to be done on all keypoints in the training database, hence, the amount of matching time is huge. Since the...
Categorizing visitors based on their navigation patterns on a website is a key problem in electronic logistics. However, user navigation data and feature vector extracted from it are sparse, and traditional clustering method doesn't solve this problem satisfactorily. As a step forward, a closed repetitive gapped subsequence mining based navigation pattern clustering method is proposed. Feature vector...
A new photo retrieval system for mobile devices is proposed in this paper. The system can be used to search for photos with similar spatial layout effectively and efficiently, and it adopts a new algorithm that extracts features of image regions based on hardware K-Means clustering. Since K-Means is computationally intensive for real-time applications in embedded systems, it is necessary to accelerate...
State-of-the-art object retrieval systems are mostly based on the bag-of-visual-words representation which encodes local appearance information of an image in a feature vector. A search is performed by comparing query object's feature vector with those for database images. However, a database image vector generally carries mixed information of an entire image which may contain multiple objects and...
A methodology for clustering multi-relational data is proposed. Initially, tuple linkages in the database schema of the multi-relational entities are leveraged to virtually organize the available relational data into as many transactions, i.e. sets of feature-value pairs. The identified transactions are then partitioned into homogeneous groups. Each discovered cluster is equipped with a representative,...
Classifying an unknown object in image retrieval systems using the nearest neighbour classifier would be very time consuming when the number of the objects within the associated database is high. Generating a dendrogram using a Hierarchical Agglomerative Clustering (HAC) algorithm and searching the database images from coarse to fine resolutions using image pyramids are two important groups of techniques...
Multivariate time series (MTS) data sets are common in many multimedia, medical, process industry and financial applications such as gesture recognition, video sequence matching, EEG/ECG data analysis or prediction of abnormal situation or trend of stock price. Multivariate time series clustering is an important task in time series data mining. The unique structure of time series makes many traditional...
Segmenting large or multiple images is time and memory consuming. These issues have been addressed in the past by implementing parallel versions of popular algorithms such as Graph Cuts and Mean Shift. Here, we propose to use an incremental Gaussian Mixture Model (GMM) learning algorithm for parallel image segmentation. We show that our approach allows us to reduce the memory requirements dramatically...
This paper presents three different ways to describe the notion concept. The first one uses the idea of hierarchy and employs a graph to define the connections between attributes and concepts. To enable concepts generation, manipulation or measurement a matrix model is developed. Thus, the entire space of terms could be generated by a set of (linearly) independent terms over a numerical field. The...
Because of the low recognition rate by affinity propagation (AP) we propose an algorithm to make face recognition based on two dimension double PCA (2DDPCA) and affinity propagation in this paper. First we use two dimension principle component analysis (2DPCA) to compress the face images in horizontal direction, then we compress the features in vertical direction using 2DPCA again. At last we use...
In this paper, we propose a new algorithm, which is using NMF features and which is different from traditional algorithms using temporal variations of the spectrogram. The proposed algorithm has been tested in several different ways by varying NMF and the speech database. The experimental results show performance of 90% and classification success of 77% for speaker dependent and independent cases,...
Music evokes various human emotions or creates music moods through low level musical features. In fact, typical music consists of one or more moods and this can be used as an important factor for determining the similarity between music. In this paper, we propose a new music retrieval scheme based on the mood change pattern. For this, we first divide music clips into segments based on low level musical...
Data mining refers to extracting or ldquominingrdquo knowledge from large amounts of data. Thus, it plays an important role in extracting spatial patterns and features. It is an essential process where intelligent methods are applied in order to extract data patterns. In this paper, we have proposed a technique with which it is possible to detect whether a given data set is erroneous. Furthermore,...
In this work, we introduced a new co-clustering based approach in content based image classification field: the two levels similarity modelling (TLSM) concept. This approach is based on a new images similarity formulation using obtained co-clusters and a wave effect which changes similarity correlations. The obtained results show a real improvement of image recognition accuracy in comparison with...
Authentication by biometric verification is becoming increasingly common in corporate, public security and other such systems. There is scads of work done in the area of offline palmprints like palmprint segmentation, crease extraction, special areas, feature matching etc. But to the best of our knowledge no work has been done yet to extract and identify the right hand of a person, given his/her left...
Clustering Web search result is a promising way to help alleviate the information overload for Web users. In this paper, we focus on clustering snippets returned by Google Scholar. We propose a novel similarity function based on mining domain knowledge and an outlier-conscious clustering algorithm. Experimental results showed improved effectiveness of the proposed approach compared with existing methods.
Solving mathematical problems is both challenging and difficult for many students. This paper proposes a document retrieval approach to help solve mathematical problems. The proposed approach is based on Kohonenpsilas Self-Organizing Maps for data clustering of similar mathematical documents from a mathematical document database. Based on a user query problem, similar mathematical documents with their...
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