Advances in Signal Processing and Intelligent Recognition by Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay

By Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay

This edited quantity incorporates a collection of refereed and revised papers initially offered on the overseas Symposium on sign Processing and clever popularity platforms (SIRS-2014), March 13-15, 2014, Trivandrum, India. this system committee acquired 134 submissions from eleven nations. every one paper used to be peer reviewed by way of no less than 3 or extra self sustaining referees of this system committee and the fifty two papers have been ultimately chosen. The papers provide stimulating insights into development popularity, computer studying and Knowledge-Based structures sign and Speech Processing picture and Video Processing cellular Computing and purposes and desktop imaginative and prescient. The e-book is directed to the researchers and scientists engaged in numerous box of sign processing and comparable components.

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All experiments are done by down sampling 165 × 120 cropped images to an order of 50 × 40. First, the proposed method is evaluated without partition. To handle large contiguous occlusion, we also proposed classical partition based approach.

Where W pca = (4) 26 N. K. Agrawal, and A. Jaiswal Enhanced Fisher Linear Discriminant (EFLD) In the original formulation of FLD, all the features for Sb and Sw were treated equally. However, the features possess different scales and importance which are useful for classification. Keeping this in mind, An and Ruan [1] proposed Enhanced FLD (EFLD) method in which the scale of features Φ j (j = 1, 2,…, n) was used to find their importance and is defined as: Φj = 1 N −1 N −1  (x ( j) i − μ( j) )2 (5) i =1 Here μ ( j ) is mean of feature j.

75 30 N. K. Agrawal, and A. Jaiswal Table 3 Classification Accuracy on JAFFE Dataset Regression based methods Training GLR LLRWO LLRO Ridge Robust Reg. Reg. 84 Table 4 Classification Accuracy on FEEDTUM Dataset Regression based methods Training GLR LLRWO LLRO Ridge Robust Reg. Reg. 43 Table 5 Ranking of the methods Method GLR LLRWO LLRO Ridge Robust FLD Fisherfaces EFLD EDA Reg. Reg. 38 Rank 2 3 5 4 6 9 7 1 8 Pj = 1 n k × ni nk ni  p k ij (11) k =1 i =1 where nk is the number of datasets and ni is the number of instances compared (nk = 3 and ni = 7 in our experiments).

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