A deep neural network based hashing for efficient image retrieval

Classification of Bisyllabic Lexical Stress Patterns Using Deep Neural Networks

Deep neural networks for texture classification—A theoretical analysis

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity.

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.

Hashing could significantly accelerate large-scale image search by transforming the high-dimensional features into binary Hamming space, where efficient similarity search can be achieved with very fast Hamming distance computation and extremely low storage cost. As an important branch of hashing methods, multi-view hashing takes advantages of multiple features from different views for binary hash learning. However, existing multi-view hashing methods are either based on shallow models which fail to fully capture the intrinsic correlations of heterogeneous views, or unsupervised deep models which suffer from insufficient semantics and cannot effectively exploit the complementarity of view features. In this paper, we propose a novel Deep Collaborative Multi-view Hashing (DCMVH) method to deeply fuse multi-view features and learn multi-view hash codes collaboratively under a deep architecture. DCMVH is a new deep multi-view hash learning framework. It mainly consists of 1) multiple view-specific networks to extract hidden representations of different views, and 2) a fusion network to learn multi-view fused hash code. DCMVH associates different layers with instance-wise and pair-wise semantic labels respectively. In this way, the discriminative capability of representation layers can be progressively enhanced and meanwhile the complementarity of different view features can be exploited effectively. Finally, we develop a fast discrete hash optimization method based on augmented Lagrangian multiplier to efficiently solve the binary hash codes. Experiments on public multi-view image search datasets demonstrate our approach achieves substantial performance improvement over state-of-the-art methods.

Hashing methods have been widely used in large-scale image retrieval. However, the constraints on the hash codes of similar images learned by the previous hashing methods are too strong, which may lead to overfitting and difficult convergence. Besides, the binary codes output by the previous hashing methods are not optimally compatible with the multi-index approach, which is the most effective method for Hamming distance query acceleration. In this paper, we propose a novel Robust and Index-Compatible Deep Hashing (RICH) method to learn compact similarity-preserving binary codes, which focuses on improving the retrieval accuracy and time efficiency simultaneously. With the learned binary codes, we can achieve better results compared with the state-of-the-arts in retrieval accuracy. Meanwhile, remarkable promotions of the retrieval time efficiency have been made in the Hamming distance query process.

Detecting pneumonia is a demanding task which always requires looking at chest X-ray images of patients suffering from it. The normal chest X-ray, reveals clear lungs without any areas of abnormal obscurity or opacification in the image. The Bacterial pneumonia, regularly consists of a focal lobar consolidation (a lung tissue which is filled with liquid instead of air) whereas the viral pneumonia shows distinct and more diffuse ‘interstitial’ pattern in both lungs. These characteristics can be identified, only with the help of an experienced radiologist. But these characteristics of pneumonia can also be overlapped with other diseases, further complicating the diagnosis. Our objective is to build a Convolutional Neural Network (CNN, or ConvNet) classifier to detect pneumonia in X-ray images of the patient. The dataset for the images is taken from kaggle—a data science learning and competition platform. Convolutional Neural Network (CNN or ConvNet) is a class of deep neural networks that specialises in analysing images and thus is widely used in computer vision applications such as image classification and clustering, object detection and neural style transfer. The above-stated objective will be implemented using Python as a programming language and using concepts of deep learning and neural networks. The dataset consists of 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are approximately 5000 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).

In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets. While the complex image appearance variations still pose a great challenge to reliable retrieval, in light of the recent progress of Convolutional Neural Networks (CNNs) in learning robust image representation on various vision tasks, this paper proposes a novel Deep Supervised Hashing (DSH) method to learn compact similarity-preserving binary code for the huge body of image data. Specifically, we devise a CNN architecture that takes pairs of images (similar/dissimilar) as training inputs and encourages the output of each image to approximate discrete values (e.g. +1/-1). To this end, a loss function is elaborately designed to maximize the discriminability of the output space by encoding the supervised information from the input image pairs, and simultaneously imposing regularization on the real-valued outputs to approximate the desired discrete values. For image retrieval, new-coming query images can be easily encoded by propagating through the network and then quantizing the network outputs to binary codes representation. Extensive experiments on two large scale datasets CIFAR-10 and NUS-WIDE show the promising performance of our method compared with the state-of-the-arts.

Deep hashing methods utilize an end-to-end framework to mutually learn feature representations and hash codes, thereby achieving a better retrieval performance. Traditional supervised hashing methods adopt handcrafted features for hashing function learning and then generate hash codes through classification and quantization. The lack of adaptability and independence of the quantization procedure leads to low retrieval accuracy of supervised hashing methods with handcrafted features in image retrieval. In this study, a non-relaxation deep hashing method for fast image retrieval is proposed. In this method, a differentiable host thresholding function is used to encourage hash-like codes to approach -1 or 1 non-linearly at the output of the convolutional neural, instead of the symbol function for quantization used in the traditional method. The output of the host thresholding function is directly used to compute the network training error, and a loss function is elaborately designed with the norm to constrain each bit of the hash-like code to be as binary as possible. Finally, a symbol function is added outside the trained network model to generate binary hash codes for image storage and retrieval in a low-dimensional binary space. Extensive experiments on two large-scale public datasets show that our method can effectively learn image features, generate accurate binary hash codes, and outperform state-of-the-art methods in terms of the mean average precision.

The continuous rise of the COVID-19 Omicron cases despite the vaccination program available has been progressing worldwide. To mitigate the COVID-19 contraction, different contact tracing applications have been utilized such as Thai Chana from Thailand. This study aimed to predict factors affecting the perceived usability of Thai Chana by integrating the Protection Motivation Theory and Technology Acceptance Theory considering the System Usability Scale, utilizing deep learning neural network and random forest classifier. A total of 800 respondents were collected through convenience sampling to measure different factors such as understanding COVID-19, perceived severity, perceived vulnerability, perceived ease of use, perceived usefulness, attitude towards using, intention to use, actual system use, and perceived usability. In total, 97.32% of the deep learning neural network showed that understanding COVID-19 presented the most significant factor affecting perceived usability. In addition, random forest classifier produced a 92% accuracy with a 0.00 standard deviation indicating that understanding COVID-19 and perceived vulnerability led to a very high perceived usability while perceived severity and perceived ease of use also led to a high perceived usability. The findings of this study could be considered by the government to promote the usage of contact tracing applications even in other countries. Finally, deep learning neural network and random forest classifier as machine learning algorithms may be utilized for predicting factors affecting human behavior in technology or system acceptance worldwide.

Similarity search in large-scale dataset has been the key technology in the fields of computer vision and multimedia in recent years. In order to support fine-grained image analysis and image retrieval, semantic segmentation based multi-instances search is becoming an urgent research issue. In this paper, a distance metric learning for semantic segmentation based graph hashing (MLSS) approach is proposed, which learns an optimal distance metric by minimizing the objective function to preserve the semantic similarity between the labels and instances. Then, an alternating optimization strategy is designed for the objective function by using gradient descent method and Rayleigh-Ritz theorem. Finally, we use the anchor graph hashing method to hash all instances into binary codes for similarity search. Multiple experiments are conducted on the public datasets and the comparative analysis validate the advantage of our proposed approach over the existing hashing methods.

Hash method with efficient retrieval efficiency and lower storage space, in the most recent has been widely used in image retrieval task. For complex image retrieval task, the manually extracted features often has limitations, and the deep convolution neural network can extract better features. However, traditional convolution neural networks usually focus on local features, while ignoring the ability to learn global features. In addition, traditional hash methods require constructing sample pairs to learn distance metrics, resulting in high computational costs. To address the above problem, in this paper a new self-attention based deep hash (SADH) learning method is proposed, which introduces a labeled hashing center and trains the network using self-attention mechanism. The method aims to fit the image hash codes to the relevant hashing center. The distance between the hashing center and the image hash codes is calculated using a loss function, resulting in generated hash code with strong discriminative power. Experimental evaluations on standard datasets demonstrate that this method outperforms the state-of-the-art retrieval methods in terms of retrieval accuracy.

Hashing has drawn more and more attention in image retrieval due to its high search speed and low storage cost. Traditional hashing methods project the high-dimensional hand-crafted visual features to compact binary codes by linear or non-linear hashing functions. Deep hashing methods, which integrate image representation learning and hash functions learning into a unified framework, have shown more superior performance. Most of existing supervised deep hashing methods mainly consider the semantic similarities among images by using pair-wise or triplet-wise constraints as supervision information. However, as a kind of crucial information, the rankings of the retrieval results, are neglected. Consequently, the produced hash codes may be suboptimal. In this paper, a new Deep Hashing with Top Similarity Preserving (DHTSP) method is proposed to optimize the quality of hash codes for image retrieval. Specifically, we utilize AlexNet to extract discriminative image representations directly from the raw image pixels and learn hash functions simultaneously. Then a top similarity preserving loss function is designed to preserve the similarity of returned images at the top of the ranking list. Experimental results on three benchmark datasets show that our proposed method outperforms most of state-of-the-art deep hashing methods and traditional hashing methods.

Deep Learning Radial Basis Function Neural Networks Based Automatic Detection of Diabetic Retinopathy

In pattern recognition, identifying Kannada handwritten numerals are a complex knot. This paper portrays an avenue that pikes us to attain a most potent Kannada Numerals recognition process. In this, handwritten Kannada characters are captivated in document fashion and are subjected to Pre-processing and attribute extraction processes. Pre-processing entails steps like noise removal, binarization, normalization, skew amendment, and thinning. Features are extricated by exploiting strategies like Drift Length Count, Direction related progression code, DWT and Curvelet Transfiguration Wrapping. For an impressive classification process deep convolution neural network classifier is preferred. Isolation accuracy of Kannada numeral aimed here will outsource 96% of accuracy.