In this paper, we demonstrate how semantic categories of images can be learnt from their color distributions using an effective probabilistic approach. Many previous probabilistic approaches are based on the Naïve Bayes that assume independence among attributes, which are represented by a single Gaussian distribution. We use a derivative of the Naïve Bayesian classifier, called Flexible Bayesia...

435 Abstract— In this paper, a simple system has been proposed to identify the type and distance of a moving vehicle using multi-classifier system (MCS). One-third octave filter bank approach has been used for extracting the significant feature from the noise emanated by the moving vehicle. The extracted features were associated with the type and distance of the moving vehicle and the heterogen...

A successful Case-Based Reasoning (CBR) system highly depends on how to design an accurate and efficient case retrieval mechanism. In this research we propose a Weighted Feature C-means clustering algorithm (WF-Cmeans) to group all prior cases in the case base into several clusters. In WF-Cmeans, the weight of each feature is automatically adjusted based on the importance of the feature to clus...

Many case-based reasoning algorithms employ derivatives of the k-nearest neighbor (k-NN) classiier for case retrieval. Several studies have shown that its similarity function is sensitive to imperfect feature sets (i.e., containing irrelevant, redundant, interacting, or noisy features). Many proposed methods attempt to reduce this sensitivity by parameterizing k-NN's similarity function with fe...

In this paper, we present a system based on feature extraction techniques and image segmentation techniques for detecting and diagnosing abnormal patterns in breast thermograms. The proposed system consists of three major steps: feature extraction, classification into normal and abnormal pattern and segmentation of abnormal pattern. Computed features based on gray-level co-occurrence matrices a...

We study a new type of queries called the k-nearest neighbor temporal aggregate (kNNTA) query. Given a query point and a time interval, it returns the top-k locations that have the smallest weighted sums of (i) the spatial distance to the query point and (ii) a temporal aggregate on a certain attribute over the time interval. For example, find a nearby club that has the largest number of people...

Accurate, up-to-date and accessible information on the state of coral reef ecosystem is necessary for informed and effective management of these important marine resources. However, environments containing these habitats are challenging to map due to their remoteness, extent and costs of monitoring. In this research, the capabilities of satellite remote sensing techniques combined with in situ ...

A resource limited immune approach (RLIA) was developed to evolve architecture and initial connection weights of multilayer neural networks. Then, with Back-Propagation (BP) algorithm, the appropriate connection weights can be found. The RLIA-BP classifier, which is derived from hybrid algorithm mentioned above, is demonstrated on SPOT multi-spectral image data, vowel data and Iris data effecti...

In [1], Aha et al. introduced a framework and methodology for machine learning, which is instances-based learning. They defined a set of rules for nearest neighbor algorithms and extended them. These algorithms are still in use for knearest neighbor classifier, which is very much related with my topic “similarity search”. In [2], Galper et al. described the similarity search and proposed a dyna...