Network-Dependent Image Annotation Based on Explicit Context-Dependent Kernel Maps

It is commonly known that the success of support vector machines in image classification and annotation it highly dependent on the relevance of the chosen kernels. The latter, defined as symmetric positive semi-definite functions, take high values when images share similar visual content and vice-versa. However, usual kernels relying only on the visual content are not appropriate in order to capture the true semantics of images which are nowadays expressed through the rich contextual cues available in image collections. Relevant kernels should instead reserve high values not only when images share similar content but also similar context. In this paper, we introduce a novel method that upgrades usual kernels and makes them context-dependent. Our kernel solution corresponds to an optimum of an energy function that trades off content and context. We will show that the proposed kernel can be expressed with an explicit mapping which is computationally efficient and also effective for image annotation. We corroborate all these statements through our participation in the recent and challenging Image CLEF 2013 annotation benchmark which ranks our method first among 58 participants' runs.