A Study on Various Dimensionality Reduction Techniques Applied in the General Shape Analysis

In Iris recognition, the identification and authentication of an individual is carried out by analysis of unique patterns of iris. This paper presents, extracting the unique features from the iris images. The feature extraction is done by using concept of energy compaction. Palacky University Database is used as a test bed for proposed iris recognition technique, which contains 384 iris images of 64 persons, with 3 images for left eye and 3 images for right eye of each person. For the proposed iris recognition technique, self mutated hybrid wavelet transform of Cosine-Walsh, Cosine-Kekre and Cosine-Sine are used to generate transformed iris images. Considering all the coefficients for 100% of energy, feature vector of transformed iris image is generated, thus the size of feature vector becomes extremely large. For proposed Iris recognition technique, 99%, 98%, 97% and 96% of partial energies are considered, thus the number of coefficients considered to generate the feature vector are very less and the size of feature vector reduces severely. Accuracy using partial energies is high as compared to 100% of energy. The self mutated hybrid wavelet transform gives improvement in Genuine Acceptance Rate (GAR) and faster recognition. The best result is obtained for Cosine-Walsh self mutated hybrid wavelet transform as compared to other combinations.

Digital images can be easily manipulated due to availability of powerful image processing software. Passive authentication as a common challenge method of digital image authentication is extensively used to detect the copy-move forgery images. In this paper, a passive authentication scheme is proposed to authenticate copy-move forgery based on discrete cosine transform (DCT). At the feature extraction step, DCT is applied to image blocks and makes use of the means of DCT coefficients to represent image blocks. The size of feature vectors are optimized. At the matching step, a set number of packages is used to store the feature vectors. The similar blocks can be found by comparing the feature vectors that are contained in adjacent packages. The experimental results demonstrate that the proposed scheme can locate irregular and meaningful tampered regions and multiply duplicated regions. In addition, it can also locate the duplicated regions in digital images that are distorted by adding white Gaussian noise, Gaussian blurring and their mixed operations.

This paper presents, efficient transform based face recognition technique which considers full and partial feature vector of an image. 2D-DCT and Walsh transform is applied on the resized image of size 128x128, to obtain its feature vector. Partial feature vector is obtained by selecting 75% rows and columns of feature vector, 50% rows and columns of feature vector and so on. The smallest size of partial feature vector is selected as 4x4. Proposed technique is tested on two different databases. Georgia Tech Face Database contains JPEG color images and Indian Face Database contains Bitmap color images of varying size. Recognition rate is calculated for varying size of selected feature vector using DCT and Walsh transform and compared. Also computational complexity in terms of number of CPU units is compared in both the cases: with full feature vector and with partial feature vector. Results show that, Walsh transform gives better recognition rate than DCT and number of CPU units required using 2D- Walsh transform is almost 9 times less than that of required by using 2D-DCT. This is because the multiplications required in Walsh transform are zero.

The paper discusses the problem of the General Shape Analysis (GSA) and considers an attempt to adapt this approach for binary silhouette analysis in the ‘SM4Public’ system. In the GSA, for a particular test shape, one or a few most similar, general templates are indicated. Shapes are represented using shape descriptors and representations are matched using similarity or dissimilarity measure. In the paper the GSA is explained and the application of the GSA in the ‘SM4Public’ system is investigated. Using a test dataset containing binary silhouettes of various objects (extracted from the ‘SM4Public’ video database) and selected shape description algorithms, an experiment was carried out. The aim of the experiment was to verify whether the GSA can be applied in the ‘SM4Public’ system as a solution for determining the class of binary silhouettes (as a preliminary classification).

Content-based search and retrieval of video data has become a challenging and important issue. Different methods[1,2,3] have been proposed to develop video retrievals systems to achieve better performance in terms of accuracy. The proposed technique uses wavelettransforms that areCosine Wavelet Transform, Walsh, Kekre, Slant and Hartley Wavelet Transform. The benefit of energy compaction of fewer transformedcoefficients is taken to reduce the feature vector size by taking fractional coefficients[5]of transformed frames of video. Smaller feature vector size results in less time for comparison of feature vectors resulting in faster retrieval of images. The feature vectors are extracted and coefficients sets are considered as feature vectors (100%, 6.25%, 3.125%, 1.5625%, 0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012%, 0.006% and 0.003% of complete transformed coefficients).The database consists of 500 videos spread across 10 categories.Performance using wavelets is compared with respect to their transforms. In all considered wavelet transform, experimental result show the performance improvement in fractional coefficient of wavelet transform in content based video retrieval as compared to 100% coefficient considered.

Thousands of images are generated everyday, which implies the need to build an easy, faster, automated classifier to classify and organize these images. Classification means selecting an appropriate class for a given image from a set of pre-defined classes. The main objective of this work is to explore feature vector generation using Walsh transform for classification. In the first method, we applied Walsh transform on the columns of an image to generate feature vectors. In second method, Walsh wavelet matrix is used for feature vector generation. In third method we proposed to apply vector quantization (VQ) on feature vectors generated by earlier methods. It gives better accuracy, fast computation and less storage space as compared with the earlier methods. Nearest neighbor and nearest mean classification algorithms are used to classify input test image. Image database used for the experimentation contains 2000 images. All these methods generate large number of outputs for single test image by considering four similarity measures, six sizes of feature vector, two ways of classification, four VQ techniques, three sizes of codebook, and five combinations of wavelet transform matrix generation. We observed improvement in accuracy from 63.22% to 74% (55% training data) through the series of techniques.

Host based Intrusion Detection Systems (HIDSs) analyze the sequence of system calls in the trace and audit log files to identify intrusive system processes. HIDSs use the frequency analysis of n-gram terms in the system call traces to identify intrusive processes. However, they are computation and resource intensive as they need to analyze a large number of n-gram input features to differentiate between normal and intrusive system processes. This put a severe limitation on their real time application while analyzing voluminous system call traces. To address this issue, we propose a computation efficient HIDS framework that initially transforms the system call traces to n-gram vector representational model and then uses a dimensionality reduction process to reduce the size of the input feature vectors. The dimensionality reduced n-gram feature vectors are finally analyzed by various machine learning based classifier models to identify intrusive processes. Performance evaluation of the proposed HIDS framework on the benchmark Australian Defense Force Academy Linux Dataset (ADFA-LD) shows that it effectively detects intrusive system processes with high accuracy and low false positive rate, while at the same time incur a minimum computational overhead.

In this study, normal (n), benign (b), and malign (m) stomach image cells have taken from faculty of Medicine the Fırat University with Light Microscope help. Total number of stomach images are 180 which be 60 n, 60 b, and 60 m. 90 of these 180 stomach images have been used for testing purposes and 90 have used for training purposes. The histograms of oriented gradient (HOG) feature vectors have been obtained for normal, benign, and malign original stomach images. The size of these HOG feature vectors is 46900×180. High-dimensional of these HOG feature vectors is reduced to lower-dimensional with Linear Discriminant Analysis (LDA). These low-dimensional data are 180×180. These low-dimensional data are classified as normal benign and malign by artificial neural network (ANN) classification. Thus, HOG_LDA_ANN method for stomach cancer images have developed. Diagnostic accuracy of classification results with this method has found as 88.9%. According to the other methods, this result has higher accuracy result. And this result has found in a shorter time.

Most of conventional object matching methods are based on comparing local features, which are too computational demanding. Recently, Dominant Orientation Templates (DOT) were proposed to solve the efficiency issue. Although DOT obtains promising results, it still suffers the problem of wasting too many bits in representation and fragility when partial occlusion occurs. As the number of templates increase, the performance will decrease. Therefore, we propose a compact DOT representation with a fast partial occlusion handling approach. Instead of using seven orientations in the original implementation, we employ single orientation of the highest gradients for the proposed compact DOT representation (C-DOT). Consequently, the size of feature vectors is reduced from 8 bits to 3 bits. To efficiently tackle the partial occlusion, we introduce the C-DOT similarity map to store the matching scores of individual grids in each sliding window, which is used to further infer the occlusion map. The experimental results demonstrate that the proposed method outperforms DOT.

Extensive work has been done on recognition of languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi, Arabic, Farsi and Chinese etc. But a very few work has been done on Odia character Recognition. There are many fields like banking, postal system, and form processing etc. Which require effective digit recognition system for faster processing. In this paper a sincere attempt has been made to do a comparative study using different types of classifiers for handwritten Odia numerals. For feature extraction gradient and curvature based approaches are used. After the calculation of feature vector Principal Component Analysis (PCA) is applied to reduce the size of feature vector. The reduced features are passed to SVM classifiers. The result obtained is compared with other classifiers such as Multilayer Neural Network (MNN), Decision tree(C5.0)and Discriminant Analysis (DA). From the experimental result it is observed that SVM based classifier achieved 90.5% accuracy with Curvature feature and 95.5% accuracy with gradient feature during validation.

With the evolution of the Internet, and the availability of image capturing devices such as advanced cameras, picture scanners, the size of advanced image accumulation is expanding quickly. Efficient image searching, browsing and retrieval devices are required by clients from different domains, including remote sensing, fashion, wrongdoing prevention, publishing, medicine, architecture, etc. Here we are extracting color mean features and color standard deviation feature with the proposed method consists of HMMD (Hue Min Max Difference) color plane. It is proved in research work that HMMD along with color mean features and color standard deviation feature is tend to reduced the size of feature vectors, storage space and gives high performance than, RGB-color mean feature. Further, HMMD color space model will be used to improve the feature extraction and improve the precision. At the end, results are presented to show the efficiency of the proposed method. Keywords: HMMD, CBIR, Feature Extraction, Precision, Recall.

Important task in image database is to organize images into appropriate category using different features of images. Image classification is studied for many years. There are various techniques proposed to increase the accuracy of classification. In this paper a novel data mining based approach is proposed for content based image classification. Feature extraction and classification algorithms are two main steps in classification process. This paper proposes the use of orthogonal transform to generate the feature vector and to investigate effectiveness of different transforms (Cosine, Sine, and Walsh). Experimentation is carried on different sizes of feature vectors which are formed by taking fractional coefficients. Classification algorithm from different families such as Bayes (Naive Bayes and Bayes Net), Function (RBFNetwork and Simple Logistic), Lazy (IB1 and Kstar), Rule (Decision and Part) and Tree (BFTree, J48 Random Tree and Random Forest) are used for classification. Experimental results and its analysis have shown the Simple Logistic classifier with Walsh transform to be better for proposed data mining based image classification technique.

Shape descriptors are more powerful to discern objects present in the images. The present work is focused on simple contour based shape descriptor using centroid distance function and it works on closed contour objects. Object area normalization is performed to obtain ‘N’ normalized contour points. The centroid distance feature extraction is performed on all normalized points. It forms simple 1-D feature vector of size ‘N’. For similarity matching correlation coefficient metric is used. This shape descriptor satisfies affine invariance property. The proposed idea is tested on MPEG-7 CE Shape-1 Part-B dataset images to validate its effectiveness. Experimental results shows that proposed compact centroid distance shape descriptor is more accurate than basic centroid distance shape descriptor and it saves space and time requirements at processing.

This paper present a robust reduced facial feature descriptor for face recognition by using dual tree complex wavelet transform and principal component analysis. Proposed approach uses extra dyadic down sampling strategy on coefficient of DT-CWT to reduce the size of feature vector and further without loss of generality principal component analysis is used on reduced feature vector significantly. Geometrical structure in facial image can be represented efficiently and effectively with low redundancy by using extra dyadic down sampling strategy. To extract facial feature this method is robust against the discrepancy of shift and illumination than the DWT. It has been verified experimentally that the proposed method is more dominant to reduce the size of feature vector.

A Multi-biometric System amalgamates the evidences collected from the multiple sources or single source for person recognition based on templates like fingerprint, palm print and iris. These evidences can be combined at various levels like pixel level, score level, feature level, and decision level. A rich set of information is present at feature level, but there is a problem of dimensionality which affects the recognition system. This study focuses on a dimensionality reduction technique Principal Component Analysis (PCA), which reduces the feature vector after feature level fusion in three different multi-biometric systems. The Pre and Post analysis of reduction compares the size of feature vector and recognition time per template in the following systems. Fingerprint based recognition where two evidences like bifurcations and ridge endings are fused, palmprint recognition where 2D-Gabor and Log-Gabor features are concatenated, and Iris Recognition in which Log-Gabor and 2D-Gabor features are integrated. And also Performance of the system is evaluated Based on False Acceptance Rate and False Rejection Rate. The study has been carried out on FVC 2004 fingerprint, IIT Delhi palmprint, and IIT Delhi Iris datasets. The results have shown that a drastic improvement in the multimodal system performance after reduction in all metrics.