The major focus of this work is on the application of indefinite kernels in multimedia processing applications illustrated on the problem of content-based digital image analysis and retrieval. The term "indefinite" here relates to kernel functions associated with non-metric distance measures that are known in many applications to better capture perceptual similarity defining relations among higher level semantic concepts. This paper describes a kernel extension of distance-based discriminant analysis method whose formulation remains convex irrespective of the definiteness property of the underlying kernel. The presented method deploys indefinite kernels rendered as unrestricted linear combinations of hyperkernels to approach the problem of visual object categorization. The benefits of the proposed technique are demonstrated empirically on a real-world image data set, showing an improvement in categorization accuracy.