Detecting Complex Image Data Using Data Mining Techniques

Detecting Complex Image Data Using Data Mining Techniques Detecting complex image data using data mining techniques IMRAN KHAN ABSTRACT The Internet, computer networks and information are vital resources of current information trend and their protection has increased in importance in current existence. The intrusion detection system (IDS) plays a vital role to monitors vulnerabilities in network and generates alerts when found attacks. Today the educational network services increasing day today so that IDS becomes essential for security on internet. The Intrusion data classification and detection process is very complex process in network security. In current network security scenario various types of Intrusion attack are available some are known attack and some are unknown attack. The attack of know Intrusion detection used some well know technique such as signature based technique and rule based technique. In case of unknown Intrusion attack of attack detection is various challenging task. In current trend of Intrusion detection used some data mining technique such as classification and clustering. The process of c lassification improves the process of detection of Intrusion. In this dissertation used graph based technique for Intrusion classification and detection. This dissertation proposes efficient intrusion detection architecture which named IDS using improved ensemble techniques (IDSIET). The IDSIET contains a new improved algorithm of attribute reduction which combines rough set theory and a method of establishing multiple rough classifications and a process of identifying intrusion data. The experimental results illustrate the effectiveness of proposed architecture. Our proposed work is implemented in MATLAB .for implementation purpose write various function and script file for implementation of our proposed architecture. For the test of our hybrid method, we used DARPA KDDCUP99 dataset. This data set is basically set of network intrusion and host intrusion data. This data provided by UCI machine learning website. Proposed method compare with exiting ensemble techniques and generate the improved ensemble technique to getting better result such as detection rate, precision and recall value. Keywords- Intrusion Detection System (IDS), IDSIET, Neural Network, rough set theory, Network Security, MATALAB, KDDCUP99 Dataset. PROPOSED METHODOLOGY AND ARCHITECTURE Comparison with linear scale-space representation While not being used explicitly in SURF, we take interest here in the approximation of Gaussian kernels by box filters to understand the advantages and the limitations of the SURF approach. 3.1 Scale-space representation linear scale space The linear scale-space representation of a real valued image u : R2 7→ R defined on a continuous domain is obtained by a convolution with the Gaussian kernel uÏÆ' := GÏÆ' âˆâ€"u (1) where GÏÆ' is the centered, isotropic and separable 2-D Gaussian kernel with variance ÏÆ'2 ∀(x,y) ∈R2, GÏÆ'(x,y) := 1 2Ï€ÏÆ'2 e−x2+y2 2ÏÆ'2 = gÏÆ'(x)gÏÆ'(y) and gÏÆ'(x) = 1 √2π ·ÃÆ'e− x2 2ÏÆ'2 . (2) The variable ÏÆ' is usually referred to as the scale parameter. Discrete scale space In practice, for the processing of a numerical image u, this continuous filter is approximated using regular sampling, truncation and normalization: ∀i,j ∈J−K,KK GÏÆ'(i,j) = 1 CK GÏÆ'(i,j) , where CK = K Xi,j =−K GÏÆ'(i,j). (3) The scale variable ÏÆ' is also sampled, generally using a power law, as discussed later in  § 3.2. Discrete box space Making use of the aforementioned box filter technique, such a multi-scale representation can be (very roughly) approximated using a box filter with square domain Γ = J−Î ³,ÃŽ ³KÃâ€"J−Î ³,ÃŽ ³K uÃŽ ³ := 1 (2ÃŽ ³ + 1)2 BΓ âˆâ€"u. (4) The question now is how to set the parameter ÃŽ ³ ∈ N to get the best approximation of Gaussian zoom-out. Second moment comparison One may for instance choose to match the second order moment ÏÆ'2 of the 1D Gaussian gÏÆ' and the variance of the corresponding box filter, as suggested by [7]. This leads to the relation ÏÆ'2 ÃŽ ³ = ÃŽ ³ Xi =−Î ³ i2 2ÃŽ ³ + 1 = (2ÃŽ ³ + 1)2 −1 12 = ÃŽ ³(ÃŽ ³ + 1) 3 , (5) where ÏÆ'2 ÃŽ ³ is the variance of the centered 1D box filter with width 2ÃŽ ³ + 1. Thus, for large values of filter size (ÃŽ ³ 1), we get approximately ÏÆ'ÃŽ ³ ≈ ÃŽ ³ √3 ≈ 0.58ÃŽ ³. Since ÃŽ ³ ∈ N takes integer values, ÏÆ'ÃŽ ³and ÏÆ' cannot match exactly in general. Moreover, due to the anisotropy of the box filter in 2D, it is impossible to match the covariance matrices. SURF scale parameter analogy Note that box filters are only used to approximate first and second order of Gaussian derivatives in SURF algorithm, and not to approximate Gaussian filtering like in [7]. However, when considering the approximation of second order Gaussian derivative Dxx GÏÆ'(x,y) = Dxx gÏÆ'(x)Ãâ€"gÏÆ'(y) = 1 ÏÆ'22 ÏÆ'2 −1gÏÆ'(x)Ãâ€"gÏÆ'(y) By these condition order box filter operator DLxx, we can see that the1D Gaussian filter gÏÆ'(y) is approximated by 1D box filter with parameter ÃŽ ³ = L−1 2. The authors of SURF claim that the corresponding Gaussian scale is ÏÆ' = 1.2 3 L ≈ 0.8ÃŽ ³for ÃŽ ³ 1, which is close but dià ¯Ã‚ ¬Ã¢â€š ¬erent to the value given by Formula (5): ÏÆ'ÃŽ ³ ≈ 0.58ÃŽ ³. Other analogies could have been made for scale variables, for instance by considering zero crossing of second order derivative of Gaussians, second moment of Gaussian derivatives, mean-square error minimization, but each one provides dià ¯Ã‚ ¬Ã¢â€š ¬erent relations. In conclusion, defining a relation between the box parameters (L and `(L)) and the Gaussian scale variable ÏÆ' seems quite arbitrary. Visual comparison Figure 8 illustrates the dià ¯Ã‚ ¬Ã¢â€š ¬erence between the linear scale-space representation obtained by Gaussian filtering and the box-space, that is its approximation by box-filters when using relation (5). While being roughly similar, the approximated scale-space exhibits some strong vertical and horizontal artifacts due to the anisotropy and the high frequencies of the box kernels. Again, while it is not being used explicitly in SURF, these artifacts may explain some of the spurious detections of the SURF approach that will be exhibited later on. 3.2 Box-space sampling Because of the dentition of first and second order box filters, the size parameter L cannot be chosen arbitrarily. The sampling values and the corresponding variables used to mimic the linear scale space analysis. The following paragraphs give more detailed explanations. Octave decomposition Alike most multi-scale decomposition approaches (see e.g. [13, 15]), the box-space discretization in SURF relies on dyadic sampling of the scale parameter L. The box length representation is therefore divided into octaves (similarly to SIFT [14, 13]), which are indexed by parameter o ∈{1,2,3,4}, where a new octave is created for every doubling of the kernel size. Note that, in order to save computation time, the filtered image is generally sub-sampled of factor two at every octave, as done for instance by SIFT [14]. As pointed out by the author of SURF [2], sub-sampling is not necessary with the use of box filters, since the computation time complexity does not depends on scale. However, while not being explicitly stated in the original paper [2], but as done in most implementations we have reviewed (for instance, this approximation is used in [3] but not in [5]), we choose to use sub-sampling to speed up the algorithm. More precisely, instead of evaluating the multi-scale operators at each pixel, we use a sampling†step† which depends on the octave level (this sampling is detailed in the next sections). Note that this strategy is consistent with the fact that the number of features is decreasing with respect to scale. Level sampling Each octave is also divided in several levels (indexed here by the parameter i ∈ {1,2,3,4}). In the usual discrete scale space analysis, these levels correspond directly to the desired sampling of the scale variable ÏÆ', which parametrizes the discretized Gaussian kernels GÏÆ' (see definition in Eq. (16)). In SURF, the relation between scale L, octave o and level i variables is L := 2o i + 1 . (6) These values are summarized in Table 2. Note that because of the non-maxima suppression involved in the feature selection, only intermediate levels are actually used to define interest points and local descriptors (i ∈{2,3}). On comparison of the box space and the linear scale space. (Top) Convolution with squared and centered box filters with radii ÃŽ ³ = 5 and ÃŽ ³ = 20 (respectively from left to right). (Middle) Corresponding Gaussian filters with respective scales ÏÆ'5 ≈ 3.16 and ÏÆ'20 ≈ 11.83, according to formula (5). Dià ¯Ã‚ ¬Ã¢â€š ¬erence between Gaussian and Box filters (using a linear transform for visualization). We can see here that the box space is a rough approximation of the Gaussian scale space, that exhibits some artifacts due to the anisotropy and the high frequencies of the box kernels. Scale analogy with linear scale space As discussed before in Section 3.1, we can define a scale analysis variable by analogy with the linear scale space decomposition. In [2], the scale parameter ÏÆ'(L) associated with octave o and level i is obtained by the following relation ÏÆ'(L) := 1.2 3(2o Ãâ€"i + 1) = 0.4L. (7) Since the relation between the scale ÏÆ'(L) of an interest point is linear in the size parameter L of box filters operators, we shall speak indià ¯Ã‚ ¬Ã¢â€š ¬erently of the former or the latter to indicate the scale. Remark A finer scale-space representation could be obtained (i.e. with sub-pixel values of L) using a bilinear interpolation of the image, as suggested in [2]. This is not performed in the proposed implementation. 3.3 Comparison with Gaussian derivative operators 3.3.1 First order operators The first order box filters DL x and DL y defined at scale L are approximations of the first derivatives of Gaussian kernel at the corresponding scale ÏÆ'(L) (see Eq. (7)), respectively corresponding to Dx GÏÆ'(x,y) = − x ÏÆ'2(L) GÏÆ'(x,y) and Dy GÏÆ'(x,y). These operators are used for local feature description, in detailed we compares the first order box filter impulse response with the discretized Gaussian derivative kernel. DL x ÃŽ ´ (Eq. (6)) Dx GÏÆ'(L) Illustration of the discrete derivative operator DL x (defined in Section 2.3.1) and discretization of the Gaussian derivative kernel Dx GÏÆ'(L) when using scale relation ÏÆ'(L) from Eq. (7). 3.3.2 The second order operators Second order dià ¯Ã‚ ¬Ã¢â€š ¬erential operators are computed in the scale-space for the detection of interest points [9, 10]. In the linear scale-space representation, this boils down to the convolution with second derivatives of Gaussian kernels Dxx GÏÆ'(x,y) = 1 ÏÆ'22 ÏÆ'2 −1GÏÆ'(x,y), Dyy GÏÆ', and Dxy GÏÆ'(x,y) = xy ÏÆ'4 GÏÆ'(x,y). (8) In the SURF approach, the convolution with theses kernels are approximated by second order box filters, previously introduced respectively as DL xx, DL yy , and DL xy . A visual comparison between second order derivatives of Gaussian and their analogous with box filters. These operators are required for local feature selection step in section 4. 3.3.3 Scale Normalization According to [12], dià ¯Ã‚ ¬Ã¢â€š ¬erential operators have to be normalized when applied in linear scale space in order to achieve scale invariance detection of local features. More precisely, as it can be seen from Equation (21), the amplitude of the continuous second order Gaussian derivative filters decreases with scale variable ÏÆ' by a factor 1 ÏÆ'2. To balance this eà ¯Ã‚ ¬Ã¢â€š ¬ect, second order operators are usually normalized by ÏÆ'2, so that we get for instance (a) (b) (c) (d) On comparison of second order box filters and second order derivative of Gaussian kernels. (a) operator DL yy; (b) discretizedsecondorderGaussianderivative D2 y GÏÆ'; (c) operator DL xy; (d) discretized second order Gaussian derivative Dxy GÏÆ'; For comparison purpose, we used again the scale relation ÏÆ'(L) from Eq. (7). †¢ the scale-normalized determinant of Hessian operator: DoHÏÆ' (u) :=uÏÆ' −(Dxy uÏÆ')2; (9) †¢ the scale-normalized Laplacian operator: à ¢Ã‹â€ Ã¢â‚¬  ÃÆ' u := ÏÆ'2à ¢Ã‹â€ Ã¢â‚¬   uÏÆ' = ÏÆ'2à ¢Ã‹â€ Ã¢â‚¬   GÏÆ' âˆâ€"u = ÏÆ'2(Dxx + Dyy)GÏÆ' âˆâ€"u = ÏÆ'2(Dxx uÏÆ' + Dyy uÏÆ'), (10) where à ¢Ã‹â€ Ã¢â‚¬  ÃÆ' GÏÆ'(x,y) = ÏÆ'2(Dxx +Dyy)à ¢- ¦GÏÆ'(x,y) =x2+y2 ÏÆ'2 −1GÏÆ'(x,y) is the multi-scale Laplacian of Gaussian. Observe that this operator can be obtained from the Trace of the scalenormalized Hessian matrix. These two operators are widely used in computer vision for feature detection. They are also approximatedinSURF,asdetailedinthenextsections. Asaconsequence, suchascale-normalization is also required with box filters to achieve similar invariance in SURF. To do so, the authors proposed that amplitude of operators DL xx , DL yy , and DL xy should be reweighted so that the l2 norms of normalized operators become constant over scales. The quadratic l2 norm of operators are estimated from the squared Frobenius norm of impulse responses kDL xxk2 2 := kDL xx ÃŽ ´k2 F = kDL yy ÃŽ ´k2 F =1 + 1 + (−1)2L(2L−1) = 6L(2L−1), so that kDL xxk2 2 ≈ 12L2 when L=1, and kDL xyk2 2 := kDL xy ÃŽ ´k2 F =1 + 1 + (−1)2 + (−1)2LÃâ€"L = 4L2. This means that box filters responses should be simply divided by the scale parameter L to achieve scale invariance detection. Interest point detection: In the previous sections, second order operators based on box filters have been introduced. These operators are multi-scale and may be normalized to yield scale invariant response. We will now take interest in their use for multi-scale local feature detection. Once the integral image has been computed, three consecutive steps are performed which are detailed in the following sections: 1. Feature filtering based on a combination of second order box filters; 2. Feature selection is combining non-maxima suppression and thresholding; 3. Scale-space location refinement ( § 4.3) using second order interpolation. This interest point detection task is summarized in Algorithm 1. Step-1 Filtering Image by Integration: Integral image and box filters Let u be the processed digital image defined over the pixel grid à ¢Ã¢â‚¬Å¾Ã‚ ¦ = [0,N-1]Ãâ€"[0.M-1], where M and N are positive integers. In the following, we only consider quantized gray valued images (taking values in the range [0; 255]), which is the simplest way to achieve robustness to color modifications, such as a white balance correction. The integral image of I for(x,y) à Ã¢â‚¬Å¾ à ¢Ã¢â‚¬Å¾Ã‚ ¦ is Flow Diagram: Figure3.1: showing the flow chart of the process for object detection Step 2: Point Detection: During the detection step, the local maxima in the box-space of the determinant of Hessian† operator are used to select interest point candidates. These candidates are then validated if the response is above a given threshold. Both the scale and location of these candidates are then refined using quadratic fitting. Typically, a few hundred interest points are detected in a megapixel image. input: image u, integral image U, octave o, level i output: DoHL(u) function Determinant_of_Hessian (U; o; i) L 2oi + 1 (Scale variable, Eq. (19)) for x := 0 to M à ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬ 1, step 2oà ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬1 do (Loop on columns) for y := 0 to N à ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬ 1, step 2oà ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬1 do (Loop on rows) DoHL(u)(x; y) Formula (24) (with (4), (10) and (11)) end for end for return DoHL(u) end function Algo input: image u output: listKeyPoints (Initialization) U IntegralImage(u) (Eq. (1)) (Step 1: filtering of features) for L 2 f3; 5; 7; 9; 13; 17; 25; 33; 49; 65g do (scale sampling) DoHL(u) Determinant_of_Hessian (U; L) end for (Step 2: selection and refinement of keypoints) for o := 1 to 4 do (octave sampling) for i := 2 to 3 do (levels sampling for maxima location) L -> 2o i + 1 listKeyPoints -> listKeyPoints + KeyPoints(o; i;DoHL(u)) end for end for return listKeyPoints So that the scale normalization factor C(L) for second order box filters should be proportional to 1 L2 However, the previous normalization is only true when L1. Indeed, while we have kDxxGÏÆ'k2 2 kDxyGÏÆ'k2 2 = 3 at any scale ÏÆ', this is not exactly true with box filters, where: kDL xxk2 2 kDL xyk2 2 = 3(2L−1) 2L ≈ 3 when L1. To account for this dià ¯Ã‚ ¬Ã¢â€š ¬erence in normalization for small scales, while keeping the same (fast) un-normalized box filters, the author of SURF introduced in Formula (24) a weight factor: w(L) = kDL xxk2 kDL xyk2  ·kDxyGÏÆ'k2 kDxxGÏÆ'k2 =r2L−1 2L . (26) The numerical values of this parameter are listed in the last column of Table 2. As noticed by the authors of SURF, the variable w(L) does not vary so much across scales. This is the resaon why the weighting parameter w in Eq. (10) is fixed to w(3) = 0.9129. Feature selection: In our methodology, interest points are defined as local maxima of the aforementioned DoHL operator applied to the image u. These maxima are detected by considering a 3 Ãâ€" 3 Ãâ€" 3 neighborhood, andperforminganexhaustivecomparisonofeveryvoxelofthediscretebox-spacewith its 26 nearest-neighbors. The corresponding feature selection procedure is described in Algorithm 3. Algorithm 3 Selection of features input: o,i,DoHL(u) (Determinant of Hessian response at octave o and level i) output: listKeyPoints (List of keypoints in box space with sub-pixel coordinates (x,y,L)) function KeyPoints (o,i,DoHL(u)) L ↠ 2oi + 1 for x := 0 to M −1, step 2o−1 do (Loop on columns) for y = 0 to N −1, step 2o−1 do (Loop on rows) if DoHL(u)(x,y) > tH then (Thresholding) if isMaximum (DoHL(u),x,y) then (Non-maximum suppression) if isRefined (DoHL(u),x,y,L) then addListKeyPoints (x,y,L) end if end if end if end for end for return listKeyPoints end function Remark A faster method has been proposed in [21] to find the local maxima without exhaustive search, which has been not implemented for the demo. Thresholding: Using four octaves and two levels for analysis, eight dià ¯Ã‚ ¬Ã¢â€š ¬erent scales are therefore analyzed (see Table 2 in Section 3.2). In order to obtain a compact representation of the image -and also to cope with noise perturbation- the algorithm selects the most salient features from this set of local maxima. This is achieved by using a threshold tH on the response of the DoHL operator DoHL(u)(x,y) > tH . (27) Note that, since the operator is scale-normalized, the threshold is constant. In the demo, this threshold has been set to 10 assuming that the input image u takes values in the intervalJ0,255K. This setting enables us to have a performance similar to the original SURF algorithm [2, 1] (see Section 6 for more details). Figure 13 shows the set of interest points detected as local box-space maxima of the DoHL operator, and selected after thresholding. For visualization purpose, the radii of the circles is set as 2.5 times the box scale L of the corresponding interest points.

Propaganda In Elections Essay -- essays research papers

Propaganda In Elections Have you ever seen a TV commercial portraying a disastrous automobile accident, and then you reminds you to wear your seatbealts?!?! Believe it or not, that's using a technique in propaganda called the fear appeal. Propaganda is more widespread than people picture. Propaganda is being used for everything from the baby food you feed your child to the TV commercial you laughed at yesterday night. There are many techniques that a propagandist can use to seduce you. Some of the best known styles in propaganda are Plain Folk, Fear, Name Calling, and Glittering Generality, In this year's elections, propaganda has played an important role in who was elected. This year's presidential candidates were all millionaires, but they have gone to great lengths to present themselves as ordinary citizens. Bill Clinton eats at Mc Donald's and read a variety of spy novels. Bob Dole presents himself as the "all American boy" from the Heartland. In this two examples the plain folk device is at work. When either presidential candidates agitates the public's fear of immigration, taxes, or crime and voting for him will reduce the threat he is using the Fear Appeal. By playing on the public's deep-seated fears, practitioners of this technique hopes to redirect the merits of a proposal and towards steps that can be tak...

The DIM Lighting Co. Case Analysis Form Essay

I. Problems: Macro: 1. The DIM Lighting Co. has had a decline of 15% in profit margins over the past year. 2. This subsidiary is part of a large corporation and operates as a profit center. 3. The company wants to stay competitive and profitable in today’s economy. New technologies are being developed by the competitors. Micro: 1. The proposed research project is considered â€Å"high risk† by members of management. Corporate is supportive of the idea but not ready to commit to the amount of money needed. 2. The initial investment for the â€Å"Light of the Future† is $1.2 million per year for the next two years with an additional $500,000 to begin production. 3. Money is needed for new equipment for current product which has an immediate payback. 4. Management is not confident in the financial figures provided by the accounting department. 5. The company needs to stay competitive while keeping up with current production. II. Causes 1. The company has had a 15% decline in profit margins over the past year. 2. The company is trying to develop new products while keeping up with current production. 3. The new â€Å"Light of the Future† is considered a risky investment and management is worried about the amount of money needed to develop new product. 4. The company is also concerned on the amount of time required before payback on new product is feasible. 5. The management team is not confident in the financial figures presented at the meeting. III. Alternatives 1. The management team needs to feel confident in the financial numbers presented by accounting. Without the confidence, an accurate decision cannot be made. Accounting needs to review and resubmit numbers to management team. 2. Also company needs total support and capital from corporate. A feasibility review of the project and its financial investment  is needed before proceeding. 3. Research and Development may need to look at other potential projects that will have the same profitability but requires smaller investment and quicker payback period. IV. Recommendations 1. Review current financial records to gain confidence in numbers. 2. Review by R&D to see if any reductions can be made without sacrificing product quality and profitability. 3. R&D to research additional potential products for future production. 4. Insure that current production is meeting current customer requirements. Also look for cost savings in current production to offset 15% decline. 5. Once these items are completed, decision made and presented to corporate for support and capital for investment.

The Failure of Modern Capitalism Looking at Modern...

In a time of such economic distress, where it sometimes feels as though the world as we know it is coming to an end, it is hard not to examine and question the fundamental idea of capitalism. Many people are starting to wonder if our laissez-faire attitude towards the economy can continue to be successful or if a major change to our society is imminent. In seeking answers to these questions, it is impossible not to think of economist Karl Marx, who spent the better part of his career analyzing capitalism. Marx long ago predicted what he deemed the inevitable downfall of the capitalist society and outlined his reasoning in his infamous Manifesto of the Communist Party. According to Marx, capitalism is bound to collapse due to its inherent†¦show more content†¦[Marx 1983:210] Quite simply, the capitalist system eventually reaches a point where it is so productive that more is produced than people can buy. When companies are unable to sell all their product they must cut costs in another area, which almost always results in laying off workers. This then creates more people who are unable to afford to purchase products, both from the original company and other companies as well. The value of these products becomes so low that companies are forced to destroy their excess product in order to insure the rest of the product retains value. An example of this in our current economy is the real estate crisis. The New York Times article â€Å"Vacancies Raise Risk and Cut Value of Real Estate† looks specifically at the struggling real estate market of commercial buildings in major cities across the United States. The article discusses the trouble the building owners are running into in finding tenants and how they are consequently being forced to offer space at extremely low prices or to sell their buildings all together. 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