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Full-Text Articles in Computer Engineering
Combined Feature Compression Encoding In Image Retrieval, Lu Huo, Leijie Zhang
Combined Feature Compression Encoding In Image Retrieval, Lu Huo, Leijie Zhang
Turkish Journal of Electrical Engineering and Computer Sciences
Recently, features extracted by convolutional neural networks (CNNs) are popularly used for image retrieval. In CNN representation, high-level features are usually chosen to represent the images in coarse-grained datasets, while mid-level features are successfully applied to describe the images for fine-grained datasets. In this paper, we combine these different levels of features as a joint feature to propose a robust representation that is suitable for both coarse-grained and fine-grained image retrieval datasets. In addition, in order to solve the problem that the efficiency of image retrieval is influenced by the dimensionality of indexing, a unified subspace learning model named spectral …
Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay
Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay
Turkish Journal of Electrical Engineering and Computer Sciences
The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to …
Elimination Of Useless Images From Raw Camera-Trap Data, Ulaş Tekeli̇, Yalin Baştanlar
Elimination Of Useless Images From Raw Camera-Trap Data, Ulaş Tekeli̇, Yalin Baştanlar
Turkish Journal of Electrical Engineering and Computer Sciences
Camera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals. To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast …
Spatial-Aware Global Contrast Representation For Saliency Detection, Dan Xu, Shucheng Huang, Xin Zuo
Spatial-Aware Global Contrast Representation For Saliency Detection, Dan Xu, Shucheng Huang, Xin Zuo
Turkish Journal of Electrical Engineering and Computer Sciences
Deep learning networks have been demonstrated to be helpful when used in salient object detection and achieved superior performance than the methods that are based on low-level hand-crafted features. In this paper, we propose a novel spatial-aware contrast cube-based convolution neural network (CNN) which can further improve the detection performance. From this cube data structure, the contrast of the superpixel is extracted. Meanwhile, the spatial information is preserved during the transformation. The proposed method has two advantages compared to the existing deep learning-based saliency methods. First, instead of feeding the deep learning networks with raw image patches or pixels, we …