The amount of digital data created and shared via internet has been increasing every day. The number of security attacks and threats has been increased due to poor selection of secret keywords and passwords.Cryptographic algorithms and security protocols are primarily rely on random keys to provide security services.Random numbers and key stream are playing major role in applying the security mechanisms in real time. In this paper, a novel method to generate random key stream using word grid puzzle is proposed.The proposed method is experimented and a minor analysis has been performed in the obtained result.
Assigning a label pertaining to an image belonging to its category is defined as object taxonomy. In this paper, we propose a transform based descriptor which effectively extracts intensity gradients defining edge directions from segmented regions. Feature vectors comprising color, shape and texture information are obtained in compressed and de-correlated space. Firstly, Fuzzy c-means clustering is applied to an image in complex hybrid color space to obtain clusters based on color homogeneity of pixels. Further, HOG is employed on these clusters to extract discriminative features detecting local object appearance which is characterized with fine scale gradients at different orientation bins. To increase numerical stability, the obtained features are mapped onto local dimension feature space using PCA. For subsequent classification, diverse similarity measures and Neural networks are used to obtain an average correctness rate resulting in highly discriminative image classification. We demonstrated our proposed work on Caltech-101 and Caltech-256 datasets and obtained leading classification rates in comparison with several benchmarking techniques explored in literature.
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