Boundary Matching and Interior Connectivity-Based Cluster Validity Anlysis

The evaluation of clustering results plays an important role in clustering analysis. However, the existing validity indices are limited to a specific clustering algorithm, clustering parameter, and assumption in practice. In this paper, we propose a novel validity index to solve the above problems based on two complementary measures: boundary points matching and interior points connectivity. Firstly, when any clustering algorithm is performed on a dataset, we extract all boundary points for the dataset and its partitioned clusters using a nonparametric metric. The measure of boundary points matching is computed. Secondly, the interior points connectivity of both the dataset and all the partitioned clusters are measured. The proposed validity index can evaluate different clustering results on the dataset obtained from different clustering algorithms, which cannot be evaluated by the existing validity indices at all. Experimental results demonstrate that the proposed validity index can evaluate clustering results obtained by using an arbitrary clustering algorithm and find the optimal clustering parameters.

Modified Boundary Matching algorithm for Error detection and Error Concealment for Video Communication

As the demand of video transmission over communication network has grown rapidly, the data compression and error correction in video processing have shown significant improvement day by day. When the error occurs in a single frame, the visual quality of the subsequent frames gets degraded due to error propagation. Thus, the error control techniques are required for the recovery. Concealment of error at the receiver (decoder) side feats the spatial and temporal characteristics of the frame. Without the requirement of the extra bandwidth and retransmission delay, it enhances the quality of the reconstructed video. However, the output of the error concealment may get affected if the error located before is misleading. Thus error detection also plays an important role while reconstructing the video. However, the output of the error concealment may get affected if the error located before is misleading. This paper proposes error detection and concealment approach for the recovery of lost Macro Block (MB) in video. The spatio-temporal techniques has been used for the error detection followed by the MB type decision applied for classifying the damaged macro block .For the concealment method a new method i.e. Modified Spatio-Temporal Boundary Matching Algorithm (MSTBMA) has been proposed. The proposed work is compared with various existing method for spatial and temporal error concealment. The comparison has been done for various types of error such as block error (single, multiple), burst error and random error generated by the software. Performance is improves in terms of PSNR and visual quality by considering the type of lost MB.