Data Mining Practical Machine Learning Tools and Techniques

A prevalent and pervasive view of designing is that it can be modeled using variables and decisions made about what values should be taken by these variables. The activity of designing is carried out with the expectation that the designed artifact will operate in the natural world and the social world. These worlds impose constraints on the variables and their values; so, design could be described as a goal-oriented, constrained, decision- making activity. However, design distinguish- es itself from other similarly described activities not only by its domain but also by additional necessary features. Designing involves exploration, exploring what variables might be appropriate. The process of explo- ration involves both goal variables and deci- sion variables. In addition, designing involves learning: Part of the exploration activity is learning about emerging features as a design proceeds. Finally, design activity occurs within two contexts: the context within which the designer operates and the context produced by the developing design itself. The designer’s perception of what the context is affects the implication of the context on the design. The context shifts as the designer’s perceptions change. Design activity can be now characterized as a goal-oriented, con- strained, decision-making, exploration, and learning activity that operates within a con- text that depends on the designer’s percep- tion of the context.

Design Prototypes: A Knowledge Representation Schema for Design

The intent of this chapter is to enhance understanding of various dietary assessment methods so that the most appropriate method for a particular need is chosen. This review focuses only on individual level food intake assessment. It is intended to serve as a resource for those who wish to assess diet in a research study using individual measurements for group level analysis. The chapter reviews major dietary assessment methods, their advantages and disadvantages, and validity; describes which dietary assessment methods are appropriate for different types of studies and populations; and discusses specific issues that relate to all methods.

Dietary Assessment Methodology

One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.

A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

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Determining Image Origin and Integrity Using Sensor Noise

In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estimating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.

Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier’s confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.

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Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment

https://engineering.purdue.edu/~prints/public/papers/dfrws06-nitin.pdf

A survey of forensic characterization methods for physical devices

Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we describe a new approach to food identification using several features based on local and global measures and a “voting” based late decision fusion classifier to identify the food items. Experimental results on a wide variety of food items are presented.

/pdf/combining-global-and-local-features-for-food-identification-2wdclis0z0.pdf

Combining global and local features for food identification in dietary assessment

Digital images can be captured or generated by a variety of sources including digital cameras and scanners. In
many cases it is important to be able to determine the source of a digital image. This paper presents methods for
authenticating images that have been acquired using flatbed desktop scanners. The method is based on using the
pattern noise of the imaging sensor as a fingerprint for the scanner, similar to methods that have been reported
for identifying digital cameras. To identify the source scanner of an image a reference pattern is estimated for
each scanner and is treated as a unique fingerprint of the scanner. An anisotropic local polynomial estimator is
used for obtaining the reference patterns. To further improve the classification accuracy a feature vector based
approach using an SVM classifier is used to classify the pattern noise. This feature vector based approach is
shown to achieve a high classification accuracy.

/pdf/scanner-identification-using-sensor-pattern-noise-2qc2d2lqtc.pdf

Nitin Khanna

Papers

Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model

Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment

A survey of forensic characterization methods for physical devices

Combining global and local features for food identification in dietary assessment

Scanner identification using sensor pattern noise