Investigating the identity, distribution, and evolution of bird species is important for both biodiversity assessment and environmental conservation. The discrete wavelet transform (DWT) has been widely exploited to extract time-frequency features for acoustic signal analysis. Traditional approaches usually compute statistical measures (e.g., maximum, mean, standard deviation) of the DWT coefficients in each subband independently to yield the feature descriptor, without considering the intersubband correlation. A new acoustic descriptor, called the local wavelet acoustic pattern (LWAP), is proposed to characterize the correlation of the DWT coefficients in different subbands for birdsong recognition. First, we divide a variable-length birdsong segment into a number of fixed-duration texture windows. For each texture window, several LWAP descriptors are extracted. The vector of locally aggregated descriptors (VLAD) is then used to aggregate the set of LWAP descriptors into a single VLAD vector. Finally, principal component analysis (PCA) plus linear discriminant analysis (LDA) are employed to reduce the feature dimensionality for classification purposes. Experiments on two birdsong datasets show that the proposed LWAP descriptor outperforms other local descriptors, including linear predictive coding cepstral coefficients, Mel-frequency cepstral coefficients, perceptual linear prediction cepstral coefficients, chroma features, and prosody features. Furthermore, the proposed LWAP descriptor, followed by VLAD encoding, PCA plus LDA feature extraction, and a simple distance-based classifier, yields promising results that are competitive with those obtained by the state-of-the-art convolutional neural networks.

The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method.

Eigenspace projection methods are widely used for feature extraction from hyperspectral images (HSI) for the classification of land cover. Projection transformation is used to reduce higher dimensional feature vectors to lower dimensional vectors for more accurate classification of land cover types. In this paper, a nearest feature line embedding (NFLE) transformation is proposed for the dimension reduction (DR) of an HSI. The NFL measurement is embedded in the transformation during the discriminant analysis phase, instead of the matching phase. Three factors, including class separability, neighborhood structure preservation, and NFL measurement, are considered simultaneously to determine an effective and discriminating transformation in the eigenspaces for land cover classification. Three state-of-the-art classifiers, the nearest-neighbor, support vector machine, and NFL classifiers, were used to classify the reduced features. The proposed NFLE transformation is compared with different feature extraction approaches and evaluated using two benchmark data sets, the MASTER set at Au-Ku and the AVIRIS set at Northwest Tippecanoe County. The experimental results demonstrate that the NFLE approach is effective for DR in land cover classification in the field of Earth remote sensing.

In this paper, a novel technique, known as nearest feature space (NFS) approach, is proposed for supervised classification of multisource images for the purpose of landslide hazard assessment. It is developed for land cover classification based on the fusion of remotely sensed images of the same scene collected from multiple sources. This approach presents a framework for data fusion of multisource remotely sensed images, which consists of two approaches, referred to as band generation process (BGP) and Fisher criterion based NFS classifier. Compared to the original NFS, we propose an improve NFS classifier which uses the Fisher criterion of between-class and within-class discrimination to enhance the original one. In the training phase, the labeled samples are discriminated by the Fisher criterion, which can be treated as a pre-processing of NFS. Finally, the classification results can be obtained by NFS algorithm. In order for the proposed NFS to be effective for multispectral images, a multiple adaptation BGP is introduced to create a new set of additional bands especially accommodated to landslide classes. Experimental results demonstrate the proposed BGP/NFS approach is suitable for land cover classification in earth remote sensing and improves the precision of image classification.

Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce the impacts, many researchers have been trying to find the best discriminant transformation in eigenspaces, either linear or nonlinear, to obtain better recognition results. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space embedding (called NFS embedding) algorithm is proposed for face recognition. The distance between a point and the nearest feature line (NFL) or the NFS is embedded in the transformation through the discriminant analysis. Three factors, including class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces. The proposed method was evaluated by several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.

This paper presents a method for automatic classification of birds into different species based on the audio recordings of their sounds. Each individual syllable segmented from continuous recordings is regarded as the basic recognition unit. To represent the temporal variations as well as sharp transitions within a syllable, a feature set derived from static and dynamic two-dimensional Mel-frequency cepstral coefficients are calculated for the classification of each syllable. Since a bird might generate several types of sounds with variant characteristics, a number of representative prototype vectors are used to model different syllables of identical bird species. For each bird species, a model selection method is developed to determine the optimal mode between Gaussian mixture models (GMM) and vector quantization (VQ) when the amount of training data is different for each species. In addition, a component number selection algorithm is employed to find the most appropriate number of components of GMM or the cluster number of VQ for each species. The mean vectors of GMM or the cluster centroids of VQ will form the prototype vectors of a certain bird species. In the experiments, the best classification accuracy is 84.06% for the classification of 28 bird species.

In this paper, a novel technique is proposed for a supervised classification of multisource images for the purpose of landslide hazard assessment. The method, known as the generalized positive Boolean function (GPBF), is developed for land cover classification based on the fusion of remotely sensed images of the same scene collected from multiple sources. It presents a framework for data fusion of multisource remotely sensed images, which consists of two approaches, referred to as the band generation process (BGP) and the positive Boolean function (PBF) classifier. The PBF classifier developed from a stack filter has been successfully applied in hyperspectral image classification. For the PBF to be effective for multispectral images, a multiple adaptation BGP is introduced to create a new set of additional bands especially accommodated to landslide classes. These bands include nonlinear normalized difference vegetation index data and morphological information in the form of digital elevation model (DEM)-derived slope values that originate from multiple sources. The performance of the proposed method is evaluated by fusing Systeme Pour l'Observation de la Terre images and DEM information for land cover classification during the post 921 Earthquake period in Taiwan. Experimental results demonstrate the proposed GPBF multiclassification approach is suitable for land cover classification in Earth remote sensing and improves the precision of image classification compared to conventional classifiers

In this paper, a fast approach is proposed to detect and correct the skew documents. In this proposed approach, a coarse skew angle /spl theta//sub e/ is first estimated to decrease the heavily computational burden. Next, the exact skew angle /spl theta/ located in the previously estimated range [/spl theta//sub e/-3, /spl theta//sub e/+3] is then determined by making use of the Hough transform method. In addition to the skew angle detection. The skew image is quickly corrected by decomposing the traditional geometric rotation into a translation and a local rotation process. A mapping table which stores the local rotation information is employed to avoid the large floating-point operations and speed up the rotation process on general-purpose computers. Experimental results are conducted to verify the feasibility and efficiency of our proposed method.

A graphic searching algorithm is proposed to find the optimal stack filter. The search of the optimal stack filter is reduced to a problem of finding a minimal path from the root node to the optimal node in the error cone graph (ECG). Two graphic searching techniques, the greedy and A* algorithms, are applied to avoid the searching an extremely large search space. Experimental results are given to show the efficiency and performance of our proposed method.

Searching via error code graph (ECG) to find the optimal morphological erosion filter is proposed. The problem to find the optimal solution is reduced to the problem for searching a minimal path in ECG. Since this graph satisfies some greedy properties, only a few nodes need to be traversed and examined in this graph.>