人工知能の翻訳サンプル

The age estimation algorithm realises hierarchical approach (fig. 10). First of all input fragments are divided for three age groups: smaller than 18 years old, from 18 - 45 years old and bigger than 45 years old. Afterwards the results of this in the first stage are more subdivided to seven newer groups with each limiting to one single decade. Thus the problem of multiclass classification is therefore reduced to a set of binary ‘one-against-all’ classifiers (BC).These classifiers calculate: ranks of each of the analyzed class. The total decision is obtained then by the analysis the previously received histogram of ranks.

These BCs are construction is applied with the transitioning to adaptive feature space, equal to this described earlier, and support vector machines classification of images with RBF kernel.

Input fragments were preprocessed for their luminance characteristics to align and to transform them to uniformal scale. Preprocessing includes color-space transformation and scaling, both similar to that of gender recognition algorithm. Features, calculated for each colour component, are combined to form a uniform featured vector.

Training require a huge enough coloring image database: We used state-of-the-art image databases MORPH and FG-NET with our own image database, gathered from some sources, which comprised of 10500 faces. Faces on the images were detected automatically by AdaBoost face detection algorithms.

A total number of seven thousand images were used for age classification algorithm training and testing on the first stage. 3 binary classifiers were made utilizing 144 adaptive features each of.

Classification results on the first stage are: 82 % accuracy for young age, 58 % accuracy for middle age and 92 % accuracy for senior age. Age classification rate in a three age division problem – 77.3 %.

Binary classifiers of the second stage were constructed equal to the first stage described above. A visual example of age estimation by the proposed algorithm on its first stage is presented in figs. 11.

The proposed age estimation algorithm realizes hierarchical approach (Fig. 10). First, the input fragments are divided into three age groups: less than 18 years old, 18–45 years old, and more than 45 years old. Second, the results of this first step are further subdivided into seven smaller groups, each limited to a single decade. This reduces the original multiclass classification problem to a set of binary “one-against-all” classifiers (BC). Each classifier then ranks the images based on the associated class, and the final decisions are obtained by analyzing these rank histograms.

These BCs are constructed using a two-level approach. After first transitioning to an adaptive feature space, as described earlier, the images are classified using support vector machines with radial basis function(RBF) kernels.

The input fragments are preprocessed to align and transform their luminance characteristics to a uniform scale. This preprocessing step includes color-space transformation and scaling, both operations similar to those used in the gender recognition algorithm. Features are calculated for each color component and combined to form a uniform feature vector.

Training and testing require a sufficiently large color image database. Here, we combined the state-of-the-art MORPH and FG-NET image databases with our own image database, gathered from many sources and comprising 10,500 face images. The faces in the images were detected automatically by the AdaBoost face detection algorithms.

A total of 7000 images were used to train and test the first stage of the age classification algorithm. Three BCs were created, each with 144 adaptive features.

The first-stage classification results showed 82% accuracy for young faces, 58% accuracy for middle-aged faces, and 92% accuracy for elderly faces. The overall age classification accuracy for the three age categories was 77.3%.

The second-stage BCs were constructed in the same manner as the first stage (described above). Fig. 11 shows a visual example of age estimation by the first stage of the proposed algorithm.

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The proposed age estimation algorithm realisesrealizes hierarchical approach (figFig. 10). First of all, the input fragments are divided into1for three age groups: smallerless than 18 years old, from 18 - 45 years old and biggermore than 45 years old. Afterwards2Second, the results of this in the firstfirst stepage are more further subdivided tointo seven smallernewer groups, with each limiteding to onea single decade. ThusThis reduces the problem oforiginal multiclass classification is therefore reduced problem to a set of binary ‘”one-against-all’”3 classifiers (BC). These classifiers calculate:Each classifier then ranks of each of the analyzedthe images based on the associated class. The total decision is, and the final decisions are obtained then by the analysis the previously received analyzing these rank histograms of ranks.       

These BCs are constructioned4 using a two-level approach. After is appliedfirst with the transitioning to an adaptive feature space, asequal to this5 described earlier, and the images are classified using support vector machines classification of images with radial basis function (RBF) kernels.6

The Iinput fragments wereare preprocessed forto align and transform their luminance characteristics to align and to transform them to uniformala uniform scale. This pPreprocessing step includes color-space transformation and scaling, both operations similar to those used inthat of athe 7gender recognition algorithm. Features, are calculated for each colourcolor component, are and combined to form a uniform featured8 vector.

Training and testing9 require a sufficientlyhuge largeenough coloring image database: We used. Here, we combined the state-of-the-art image databases MORPH and FG-NET image databases with our own image database, gathered from some10many sources, which comprised of many sources and comprising 10,500 face images. Faces on. The faces in the images were detected automatically by the AdaBoost face detection algorithms.

A total number of seven thousand7000 images were used for to train and test the first stage of the age classification algorithm training and testing on the first stage. 3 binary classifiers. Three BCs were made utilizingcreated, each with11 144 adaptive features each of.

Classification results on the The first-stage are:classification results showed 82 % accuracy for young agefaces, 58 % accuracy for middle age-aged faces, and 92 % accuracy for seniorelderly faces. The overall age. Age classification accuracyrate in afor the three age division problem –categories was 77.3 %.12

Binary classifiers of tThe second-stage BCs were constructed in the equalsame tomanner as the first stage (described above. A). Fig. 11 shows a visual example of age estimation by the first stage of the proposed algorithm on its first stage is presented in figs. 11..