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In this paper, two extended versions of motif co-occurrence matrices (MCM) are derived and concatenated for efficient content-based image retrieval (CBIR). This paper divides the image into 2 x 2 grids. Each 2 x 2 grid is replaced with two different Peano scan motif (PSM) indexes, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. This transforms the entire image into two different images and co-occurrence matrices are derived on these two transformed images: the first one is named as "motif co-occurrence matrix initiated from top left most pixel (MCM TL )" and second one is named as "motif cooccurrence matrix initiated from bottom right most pixel (MCM BR )". The proposed method concatenates the feature vectors of MCM TL and MCM BR and derives multi motif co-occurrence matrix (MMCM) features. This paper carried out investigation on image databases i.e. Corel-1k, Corel-10k, MIT-VisTex, Brodtaz, and CMU-PIE and the results are compared with other well-known CBIR methods. The results indicate the efficacy of the proposed MMCM than the other methods and especially on MCM [19] method.

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To extract local features efficiently Jhanwar et al. proposed Motif Co-occurrence Matrix (MCM) [23] in the literature. The Motifs or Peano Scan Motifs (PSM) is derived only on a 2*2 grid. The PSM are derived by fixing the initial position and this has resulted only six PSM's on the 2*2 grid. This paper extended this approach by deriving Motifs on a 3*3 neighborhood. This paper divided the 3*3 neighborhood into cross and diagonal neighborhoods of 2*2 pixels. And on this cross and diagonal neighborhood complete Motifs are derived. The complete Motifs are different from initial Motifs, where the initial PSM positions are not fixed. This complete Motifs results 24 different Motifs on a 2*2 gird. This paper derived cross diagonal complete Motifs matrix (CD-CMM) that has relative frequencies of cross and diagonal complete Motifs. The GLCM features are derived on cross diagonal complete Motifs texture matrix for efficient face recognition. The proposed CD-CMM is evaluated face recognition rate on four popular face recognition databases and the face recognition rate is compared with other popular local feature based methods. The experimental results indicate the efficacy of the proposed method over the other existing methods.

We present a new technique for content based image retrieval by deriving a Local motif pattern (LMP) code co-occurrence matrix (LMP-CM). This paper divides the image into 2 x 2 grids. On each 2 x 2 grid two different Peano scan motif (PSM) indexes are derived, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. From these two different PSM indexes, this paper derived a unique LMP code for each 2 x 2 grid, ranges from 0 to 35. Each PSM minimizes the local gradient while traversing the 2 x 2 grid. A co-occurrence matrix is derived on LMP code and Grey level co-occurrence features are derived for efficient image retrieval. This paper is an extension of our previous MMCM approach [54]. Experimental results on popular databases reveal an improvement in retrieval rate than existing methods.

In this present work a wavelet based watermarking technique is discussed. The proposed method transforms the image into wavelet coefficients by using DWT. A Simplified Significant Wavelet Tree (SSWT) is formed with wavelet coefficients (other than at lowest level) at higher level subband descending towards lower levels. The proposed scheme quantizes the SSWT coefficients to embed a bit of watermark into the frequency part of the image. In the watermark extraction the wavelet trees are formed on the received image to retrieve the watermark bits. The proposed scheme uses adaptive casting energy at different levels and hence achieves high robustness. Various attacks were performed to test the performance and the proposed method has shown high robustness against these attacks.

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Rough sets help in finding significant attributes of large data sets and generating decision rules for classifying new instances. Though multiple regression analysis, discriminant analysis, log-it analysis and several other techniques can be used for predicting results, they consider insignificant information also for processing which may lead to false positives and false negatives. In this study, we proposed rough set based decision rule generation framework to find reduct and to generate decision rules for predicting the Decision class. We conducted experiments over data of Portuguese Banking institution. From the proposed method, the dimensionality of data is reduced and decision rules are generated which predicts deposit nature of customers by 90% accuracy.