Santosh Chapaneri

Evaluation of Chaotic Map Lattice systems for image encryption

In this paper, the performance of Chaotic Map Lattice (CML) systems used for the purpose of digital image encryption is evaluated. The design and security flaws of CML and its variants are analyzed and overcome by the proposed modified CML-M cryptosystem. The proposed algorithm achieves semantic security due to probabilistic encryption and makes use of pixel diffusion to enhance the strength of security. We evaluate the CML variants and proposed algorithm using various randomness tests and the ability to resist cryptanalytic attacks. Evaluation results demonstrate that the proposed algorithm achieves better security and removes the flaws of previous CML cryptosystems.

Analysis of windowing techniques for speech emotion recognition

Conventionally, the spectral features are derived from the DFT spectrum using the Hamming window. The spectral leakage is reduced by windowing but the variance of the spectral estimate is high. Multitaper method emphasizes on using multiple windows and frequency domain averaging. In this paper we study the impact of introduction of multitapering on the performance of Speech Emotion Recognition. Various spectral features including MFCCs are taken into consideration while the classifier used is Support Vector Machine (SVM). For the spectral features, in case of multitapering an improvement of upto 2% was found as compared to traditional Hamming window when tested on Berlin database. Impact of variable frame size, different windows and variable taper number is also studied.

Chaos based image encryption using Latin rectangle scrambling

An efficient image cryptosystem is proposed in this paper employing Latin rectangle scrambling based on 2D Hénon chaotic map. To reduce the overall number of rounds for achieving high security, bi-directional pixel diffusion is proposed using piecewise linear chaotic maps. Compared to the existing image cryptosystems, the proposed technique achieves high security in only two rounds. Various simulation results and evaluation metrics are presented for the proposed technique.

Learning visual saliency with statistical priors

Saliency is the quality by which any object or a pixel in an image stands out relative to its neighbours. Detecting such regions from an image is a crucial problem of research, since it has wide applications in advertising, automatic image compression, image thumbnailing, etc. In this paper, a salient region detection approach is proposed by using machine learning. In order to train the saliency model, low level features such as color channels and their probabilities, also probabilities using 3D color histograms, subband features along with statistical priors such as frequency prior, color prior, chance of happening (CoH) and center bias prior (CBP) are used. The proposed model is compared with existing state of art algorithms. Human eye fixation points are used to compare the models by estimating area under ROC curves. Other parameters such as precision, recall, F-measure are also used for comparison. This comparison shows that the proposed saliency model gives better performance than the existing salient region detection approaches.

Music features based on Hu moments for genre classification

Automated musical genre classification using machine learning techniques has gained popularity for research and development of powerful tools to organize music collections available on web. Mel cepstral co-efficients (MFCCs) have been successfully used in music genre classification but they do not reflect the correlation between the adjacent co-efficients of Mel filters of a frame neither the relation between adjacent co-efficients of Mel filters of neighboring frames. This leads to loss of useful features. In this work, Hu moment based features are extracted from the spectrogram to study impact of energy concentration in the spectrogram. Under different musical genres the difference in rhythm in genres drastically changes the texture of spectrogram image. This alters the energy concentration in spectrogram. Hu moments being invariant to translation, scaling as well as rotation can capture useful features from spectrogram that are not considered by the MFCCs. Since the spectral moments are computed locally, they can assess the intensity of energy concentration at certain frequencies in spectrogram and prove as distinct features in characterizing different genres of music. Hu moment based features along with conventional music features lead to an accuracy of 83.33% for classifying 5 genres.

No-reference image quality assessment using extreme learning machines

In this paper, a No-Reference Image Quality Assessment (NR-IQA) algorithm is implemented with the help of Extreme Learning Machine (ELM) using spatial and spectral features. ELMs are single hidden layer feed-forward neural networks that provides optimum solution in a single iteration, hence ELMs can be used for performing classification and regression at high speeds. Proposed NR-IQA algorithm can quantify the amount of distortion for images caused by JPEG compression, JPEG2000 compression, Additive White Gaussian Noise, Gaussian Blurring effect and Rayleighs Fast Fading effects. The proposed algorithm is evaluated using LIVE IQA database via Spearmans Ranked Ordered Correlation Coefficient (SROCC) and Root Mean Square Error (RMSE). The proposed algorithm outperforms existing NR-IQA methods.

Hyper chaotic color image cryptosystem

Security is a major concern in digital image transmission applications. In this paper, a novel color image encryption scheme is proposed to enhance security and efficiency. The proposed scheme is a single round based hyper chaotic system due to bi-directional pixel diffusion which contributes towards increased security and improved efficiency. Security analysis such as key sensitivity, histogram, information entropy, correlation coefficient and diffusion is conducted.

Multi-taper spectral features for emotion recognition from speech

In this paper, the performance of multi-taper spectral estimate is investigated relative to conventional single taper estimate for the application of emotion recognition from speech signals. Typically, a single taper/window helps in reducing bias of the estimate, but due to its high variance, the resulting spectral features tend to give poor recognition performance. The weighted averages of the multi-tapered uncorrelated eigen-spectra results in more discriminative spectral features, thus increasing the overall performance. We demonstrate that the application of six Multi-peak multi-tapers with support vector machine results in 81% classification accuracy on seven emotions from Berlin emotion database considering only spectral features, compared to 72% using conventional Hamming window method.