  Majority of the current dimensionality reduction or retrieval techniques rely
on embedding the learned feature representations onto a computable metric
space. Once the learned features are mapped, a distance metric aids the
bridging of gaps between similar instances. Since the scaled projection is not
exploited in these methods, discriminative embedding onto a hyperspace becomes
a challenge. In this paper, we propose to inwardly scale feature
representations in proportional to projecting them onto a hypersphere manifold
for discriminative analysis. We further propose a novel, yet simpler,
convolutional neural network based architecture and extensively evaluate the
proposed methodology in the context of classification and retrieval tasks
obtaining results comparable to state-of-the-art techniques.
