Fading Affect Bias: Improving the Trade-off between Accuracy and Efficiency in Feature Clustering

We present a fast and accurate center-based, single-pass clustering method, with main focus on improving the trade-off between accuracy and speed in computer vision problems, such as creating visual vocabularies. We use a stochastic Mean-shift procedure to seek the local density peaks within a single pass of the data. We also present a dynamic kernel generation along with a density test procedure that finds the most promising kernel initializations. In our algorithm, we use two data structures, namely a dictionary of permanent kernels, and a 'short memory' that is used to determine emerging kernels to be maintained and outliers to be discarded. In our experiments we make extensive comparisons with popular clustering algorithms, with respect to accuracy and efficiency.