Feature selection with spatial path coding for multimedia analysis

The selection of a subset of discriminative features for semantic recognition is crucial to making multimedia analysis more interpretable. This paper proposes a model of spatial path coding (SPC) that uses a supervised technique to select sparse features. SPC is a regularized penalty that encodes the spatial correlations of features obtained by the spatial pyramid model. In SPC, each feature dimension is considered as a vertex in a direct acyclic graph (DAG), and the spatial correlations among features are considered as directed edges associated with predefined weights. Thus, the process of supervised feature selection can be directly formulated to solve a path selection problem with minimum cost. Experiments are conducted to evaluate the performance of supervised feature selection with SPC for the tasks of scene classification and action recognition using four benchmark datasets. The results show that SPC can be used to automatically select a subgraph of the DAG with a small number of discriminative features for a certain category. In addition, the method proposed in this paper shows better performance in terms of classification and recognition accuracy as compared with state-of-the-art algorithms.