A. H. Hadjahmadi, M. M. Homayounpour, S. M. Ahadi,
Volume 8, Issue 2 (6-2012)
Abstract
Nowadays, the Fuzzy C-Means method has become one of the most popular
clustering methods based on minimization of a criterion function. However, the
performance of this clustering algorithm may be significantly degraded in the presence of
noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans
(BWFCM). We used a new objective function that uses some kinds of weights for
reducing the effect of noises in clustering. Experimental results using, two artificial
datasets, five real datasets, viz., Iris, Cancer, Wine, Glass and a speech corpus used in a
GMM-based speaker identification task show that compared to three well-known clustering
algorithms, namely, the Fuzzy Possibilistic C-Means, Credibilistic Fuzzy C-Means and
Density Weighted Fuzzy C-Means, our approach is less sensitive to outliers and noises and
has an acceptable computational complexity.
S. A. R. Seyedin, A. Shahpari,
Volume 11, Issue 2 (6-2015)
Abstract
In this paper, five conditions that have been proposed by Cobb and Shenoy are studied for nine different mappings from the Dempster-Shafer theory to the probability theory. After comparing these mappings, one of the considerable results indicates that none of the mappings satisfies the condition of invariance with respect to the marginalization process. In more details, the main reason for this defect is that the classic projection process in DST loses some probabilistic information. Therefore, as regards this subject, a solution is presented for solving this problem for two mappings: the pignistic probability and the normalized plausibility transformation.
S. Tannaz, T. Sedghi,
Volume 14, Issue 2 (6-2018)
Abstract
In this article, a fabulous method for database retrieval is proposed. The multi-resolution modified wavelet transform for each of image is computed and the standard deviation and average are utilized as the textural features. Then, the proposed modified bit-based color histogram and edge detectors were utilized to define the high level features. A feedback-based dynamic weighting of shape, color and textural features composition produce a resistant feature vectors for image retrieval and recall. A comprehensive and unified matching scheme based on matrix error rate technique was accomplished for similarity of image and retrieval procedure. The feature vectors size in our algorithm is the least one evaluated to the different techniques. Furthermore, the calculation time of previously published techniques is much more than the presented algorithm which is a benefit in proposed retrieval method. The experimental results illustrates that novel algorithm obtains more precious in retrieval and the efficiency in evaluating with the other techniques and algorithms at Corel color image database.
P. Ahmadi, I. Gholampour,
Volume 15, Issue 2 (6-2019)
Abstract
Analyzing motion patterns in traffic videos can be employed directly to generate high-level descriptions of their content. For traffic videos captured from intersections, usually, we can easily provide additional information about traffic phases. Such information can be obtained directly from the traffic lights or through traffic lights controllers. In this paper, we focus on incorporating additional information to analyze the traffic videos more efficiently. Using side information on traffic phases, the semantic of motion patterns from traffic intersection scenes can be learned more effectively. The learning is performed based on optical flow features extracted from training video clips, and applying them to supervised topic models such as MedLDA and MedSTC. Based on such models, any video clip can be represented based on the learned patterns. Such representations can be further exploited in scene analysis, rule mining, abnormal event detection, etc. Our experiments show that employing side information in intersection video analysis leads to improvement in discovering scene pattern. Moreover, supervised topic models achieve about 4% improvement in abnormal event detection, compared to the unsupervised ones, in terms of area under ROC.
