Experiments of Distance Measurements in a Foliage Plant Retrieval System

One of important components in an image retrieval system is selecting a distance measure to compute rank between two objects. In this paper, several distance measures were researched to implement a foliage plant retrieval system. Sixty kinds of foliage plants with various leaf color and shape were used to test the performance of 7 different kinds of distance measures: city block distance, Euclidean distance, Canberra distance, Bray-Curtis distance, x2 statistics, Jensen Shannon divergence and Kullback Leibler divergence. The results show that city block and Euclidean distance measures gave the best performance among the others.Comment: 14 pages, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 5, No. 2, June, 201

Pattern Recognition Bird Sounds Based on Their Type Using Discreate Cosine Transform (DCT) and Gaussian Methods

To know the type of bird, most people know from the shape of bird species and the sound of birds. In this study, it identified the pattern of bird sounds. The bird sounds studied were Canary Trills, Vulture and Crow birds. In the introduction of the type of bird sound pattern in this study using the Discrete Cosine Transform (DCT) method and Gaussian value. The researcher conducted several steps to get the sound model of birds, among others, namely (1) bird sound input in the form of WAV file, (2) Hamming Windowing, (3) DFT / FFT, (4) Mel Bank Filter, (5) DCT, and (6) Value Gaussian. The output obtained is in the form of vector values and represented in graphical form. The results obtained in the study of pattern recognition of bird sound types get the results of observations in the same bird sound duration and frequency of the same, then the same pattern is obtained in the same bird as evidenced by calculating the closest distance value with Bray Curtis method. For the same duration of time and the length of the frequency that is not the same; it found that the pattern of bird sounds is not the same

Efficiency of texture image enhancement by DCT-based filtering

International audienceTextures or high-detailed structures as well as image object shapes contain information that is widely exploited in pattern recognition and image classification. Noise can deteriorate these features and has to be removed. In this paper, we consider the influence of textural properties on efficiency of image enhancement by noise suppression for the posterior treatment. Among possible variants of denoising, filters based on discrete cosine transform known to be effective in removing additive white Gaussian noise are considered. It is shown that noise removal in texture images using the considered techniques can distort fine texture details. To detect such situations and to avoid texture degradation due to filtering, filtering efficiency predictors, including neural network based predictor, applicable to a wide class of images are proposed. These predictors use simple statistical parameters to estimate performance of the considered filters. Image enhancement is analysed in terms of both standard criteria and metrics of image visual quality for various scenarios of texture roughness and noise characteristics. The discrete cosine transform based filters are compared to several counterparts. Problems of noise removal in texture images are demonstrated for all of them. A special case of spatially correlated noise is considered as well. Potential efficiency of filtering is analysed for both studied noise models. It is shown that studied filters are close to the potential limits