Abstract :
Sparse Coding has been successfully applied to many image and object classification problems in recent years. In such tasks, the problem is commonly formulated as representing an image or signal as a sparse linear combination of dictionary elements, where such dictionary elements capture high level patterns in the data, however, relationships between the dictionary elements are generally not taken into account in such frameworks. In this paper, we address this issue on two levels. First, we incorporate high-level domain specific information within sparse coding. Second, while generating the dictionary itself, we develop a model that encourages incoherence among its components so as to build a compact dictionary. This leads to a powerful framework that improves the performance of Sparse Coding, by giving it the right kind of "guidance" for identifying objects, particularly in presence of noise and other variations. We demonstrate the efficacy of our model through extensive experiments, which show that our method meets or exceeds the performance of state-of-the-art algorithms in several image and object classification tasks.
