In this paper, we sought to determine whether fractal parameters alone are good enough in classifying astronomical images. A fuzzy membership function which follows the model of a parabola was chosen for the purpose and the success rate was found to be 73.45%. Also, we have investigated how the grade of membership functions affect the performance of a neural network classifier. For this we included the parameter, spectral flatness measure in addition to fractal dimension and the grades of both the parameters were given as input features to the neural net. It could be observed that when grades were given as inputs to the classifier, performance of the classifier has increased to 80.53%.
In medical applications, lossless coding methods are required since loss of any information is usually unacceptable. Integer to integer lifting operations help to implement the lossless compression methods. Medical images are sparser than other natural images, so that by exploiting the sparseness feature, it is possible to achieve maximum compression efficiency. This paper modifies the sparsity index which defines the degree of sparseness of images and it proposes a histogram sorting method which is more efficient than the conventional histogram packing methods in terms of compression ratio.
This work is to apply wavelet packet transformation for the recognition of isolated handwritten Malayalam (one of the south Indian languages) characters. The key idea is that count of zero crossings of wavelet transform coefficients of an image characterize it. A set of 3000 images of 20 selected characters are used for classification. All images are normalized to have same height, binarized and inverted. Two-level Wavelet packet transformation is applied on each character image. Count of zero-crossings in each of the sixteen subbands together with a structural feature forms the feature vector. Feed forward back propagation network is used for classification. We obtained about 90% accuracy in classification and recognition. Further study by including more characters and more samples is being carried out.
Automatic animal sound classification and retrieval is very helpful for bioacoustic and audio retrieval applications. In this paper we propose a system to define and extract a set of acoustic features from all archived wild animal sound recordings that is used in subsequent feature selection, classification and retrieval tasks. The database consisted of sounds of six wild animals. The Fractal Dimension analysis based segmentation was selected due to its ability to select the right portion of signal for extracting the features. The feature vectors of the proposed algorithm consist of spectral, temporal and perceptual features of the animal vocalizations. The minimal Redundancy, Maximal Relevance (mRMR) feature selection analysis was exploited to increase the classification accuracy at a compact set of features. These features were used as the inputs of two neural networks, the k-Nearest Neighbor (kNN), the Multi-Layer Perceptron (MLP) and its fusion. The proposed system provides quite robust approach for classification and retrieval purposes, especially for the wild animal sounds.
In this android era, the word spectrum is connected with trillion faces. The underutilization problem of the spectrum in most of the applications results in the paucity of this scarce resource. Cognitive radio (CR) is an astute technology implemented for the opportunistic usage of idle spectrum resource dynamically. This paper focuses on sensing of the vacant channels, and it is pre-owned for the transmission of encrypted DICOM information. The proposed algorithm involves encryption schemes like RC5, latin square image cipher, deoxyribo nucleic acid, and discrete Gould transform (DGT) to render confusion, diffusion, and permutation operations. Further, the uncorrelated cipher image is transmitted via the identified unused licensed spectrum. The proposed scheme guarantees the authorised transactions among the stack holders like patients, doctors, and hospital management systems. It also ascertains and enhances the validation of the cipher biomedical interactions among peers by assuring tamper proofing using DGT. Simulations have been conceded over universal software radio peripheral to validate the biomedical data transactions after sensing the unused spectrum. Metrics like global-local entropies, correlation coefficients, key sensitivity, chi-square tests, cropping attacks and differential attack analysis were estimated to authenticate the robustness of the projected encryption scheme.
The computational complexity of fractal image compression is mainly because of the huge number of comparisons required to find a matching domain block corresponding to the range blocks within the image. Various schemes have been presented by researchers for domain classification which can lead to significant reduction in the time spent for range-domain matching. All the schemes propose to first separate domains into different classes and then select the appropriate class for matching with selected range block. Here, we propose a dynamic classification scheme based on local fractal dimensions. The method can be experimented with other features of image blocks measured locally. In this work we have investigated the computational efficiency of multi-way search trees for storing domain information. The domains can be listed in a B+ tree ordered on one or more selected local features of each domain.
Multiple Classifier fusion is an efficient and widely useful method of improving system performance. The classifier fusion approach to musical instrument recognition system is not been widely experimented. This paper explores in depth a classifier combination approach for the instrument classification task, studied over a diverse classifier pool, which includes K-Nearest Neighbor, Gaussian Mixture Model and Multi-Layer Perceptron classifiers. All three classifiers were trained with the same feature space, comprised of spectral, temporal, harmonic, perceptual and statistical features. The classifier fusion has been done at decision level. We employ the Sum-based and Confidence-based integration strategies to combine three classifiers k-NN, MLP and GMM. Experiments conducted on a musical sound database containing 10 different Indian musical instruments sounds prove that the proposed classifier combination approaches outperform individual classifiers.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)
'%3e%3cg id='b'%3e%3cpath id='a' stroke='%23000' stroke-width='2' d='M-6-25H6m-12 3H6m-12 3H6'/%3e%3cuse xlink:href='%23a' y='44'/%3e%3c/g%3e%3cpath stroke='%23fff' d='M0 17v10'/%3e%3ccircle r='12' fill='%23cd2e3a'/%3e%3cpath fill='%230047a0' d='M0-12A6 6 0 0 0 0 0a6 6 0 0 1 0 12 12 12 0 0 1 0-24z'/%3e%3c/g%3e%3cg transform='rotate(-123.69)'%3e%3cuse xlink:href='%23b'/%3e%3cpath stroke='%23fff' d='M0-23.5v3M0 17v3.5m0 3v3'/%3e%3c/g%3e%3c/svg%3e)