Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models

The advent of large pre-trained models has brought about a paradigm shift in both visual representation learning and natural language processing. However, clustering unlabeled images, as a fundamental and classic machine learning problem, still lacks effective solution, particularly for large-scale datasets. In this paper, we propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models such as CLIP and cluster images effectively and efficiently at scale. We show that the pre-trained features are significantly more structured by further optimizing the rate reduction objective. The resulting features may significantly improve the clustering accuracy, e.g., from 57\% to 66\% on ImageNet-1k. Furthermore, by leveraging CLIP's image-text binding, we show how the new clustering method leads to a simple yet effective self-labeling algorithm that successfully works on unlabeled large datasets such as MS-COCO and LAION-Aesthetics. We will release the code in https://github.com/LeslieTrue/CPP.Comment: 21 pages, 13 figure

Unsupervised Manifold Linearizing and Clustering

We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine learning and computer vision. When the manifolds are assumed to be linear subspaces, this reduces to the classical problem of subspace clustering, which has been studied extensively over the past two decades. Unfortunately, many real-world datasets such as natural images can not be well approximated by linear subspaces. On the other hand, numerous works have attempted to learn an appropriate transformation of the data, such that data is mapped from a union of general non-linear manifolds to a union of linear subspaces (with points from the same manifold being mapped to the same subspace). However, many existing works have limitations such as assuming knowledge of the membership of samples to clusters, requiring high sampling density, or being shown theoretically to learn trivial representations. In this paper, we propose to optimize the Maximal Coding Rate Reduction metric with respect to both the data representation and a novel doubly stochastic cluster membership, inspired by state-of-the-art subspace clustering results. We give a parameterization of such a representation and membership, allowing efficient mini-batching and one-shot initialization. Experiments on CIFAR-10, -20, -100, and TinyImageNet-200 datasets show that the proposed method is much more accurate and scalable than state-of-the-art deep clustering methods, and further learns a latent linear representation of the data

Share the Crowdsensing Data with Local Crowd by V2V Communications

With an increase in the number of mobile applications, the development of mobile crowdsensing systems has recently attracted significant attention from both academic researchers and industries. In mobile crowdsensing system, the remote cloud (or back-end server) harvests all the crowdsensing data from the mobile devices, and the crowdsensing data can be uploaded immediately via 3G/4G. To reduce the cost and energy consumption, many academic researchers and industries investigate the way of mobile data offloading. Due to the sparse distribution of the WiFi APs, offloading the crowdsensing data is often delayed. In this paper, compared with offloading data via WiFi APs, we investigate the communication and sharing of crowdsensing data by vehicles near the event (such as a pothole on the road), termed as a local crowd. In such crowd, a vehicle can transmit the data to each other by vehicle-to-vehicle (V2V) communication. The crowd-based approach has a lower delay than the offloading-based approach, by considering the quality of truth discovery. We define a utility function related to the crowdsensing data shared by the local crowd in order to quantify the trade-off between the quality of the truth discovery and the user satisfaction. Our extensional simulations verify the effectiveness of our proposed schemes

A Low-Cost Vehicle Anti-Theft System Using Obsolete Smartphone

In modern society, vehicle theft has become an increasing problem to the general public. Deploying onboard anti-theft systems could relieve this problem, but it often requires extra investment for vehicle owners. In this paper, we propose the idea of PhoneInside, which does not need a special device but leverages an obsolete smartphone to build a low-cost vehicle anti-theft system. After being fixed in the vehicle body with a car charger, the smartphone can detect vehicle movement and adaptively use GPS, cellular/WiFi localization, and dead reckoning to locate the vehicle during driving. Especially, a novel Velocity-Aware Dead Reckoning (VA-DR) method is presented, which utilizes map knowledge and vehicle’s turns at road curves and intersections to estimate velocity for trajectory computation. Compared to traditional dead reckoning, it reduces accumulated errors and achieves great improvement in localization accuracy. Furthermore, based on the learning of the driving history, our system can establish individual mobility model for a vehicle and distinguish abnormal driving behaviors by a Long Short Term Memory (LSTM) network. With the help of ad hoc authentication, the system can identify vehicle theft and send out timely alarming and tracking messages for rapid recovery. The realistic experiments running on Android smartphones prove that our system can detect vehicle theft effectively and locate a stolen vehicle accurately, with average errors less than the sight range

DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration

Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range

Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model

In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring

Metabolic Profiling to Identify the Latent Infection of Strawberry by

In plant-pathogen interaction systems, plant metabolism is usually agitated in the early stages of infection and much before visible symptoms appear. To identify the latent infection of strawberry by Botrytis cinerea by metabolome profiling, a metabolomics method based on gas chromatography and mass spectrometry was applied to identify the affected metabolites and discriminate diseased plants from healthy ones. An orthogonal partial least squares (OPLS) score plot showed that the metabolic profiling well separated B. cinerea -infected strawberry plants at 2, 5, and 7 days after infection from non-infected healthy plants. Combined analysis of variance (ANOVA) and OPLS analysis revealed candidate biomarkers of plant resistance and of infection and expansion of the pathogen in the plants. Among them, hexadecanoic acid, octadecanoic acid, sucrose, β-lyxopyranose, melibiose, and 1,1,4a-Trimethyl-5,6-dimethylenedecahydronaphthalene were closely related to the early stage of disease development when symptoms were not visible. A discrimination method that could distinguish Botrytis gray mold diseased strawberry plants from healthy ones was established based on the partial least squares discriminant analysis (PLS-DA) model with a correct recognition accuracy of 100%. This research offers a good application of metabolome profiling for early diagnosis of plant disease and interaction mechanism exploration

Metabolic Mechanism of Plant Defense against Rice Blast Induced by Probenazole

The probenazole fungicide is used for controlling rice blast (Magnaporthe grisea) primarily by inducing disease resistance of the plant. To investigate the mechanism of induced plant defense, rice seedlings were treated with probenazole at 15 days post emergence, and non-treated plants were used for the control. The plants were infected with M. grisea 5 days after chemical treatment and incubated in a greenhouse. After 7 days, rice seedlings were sampled. The metabolome of rice seedlings was chemically extracted and analyzed using gas chromatography and mass spectrum (GC-MS). The GC-MS data were processed using analysis of variance (ANOVA), principal component analysis (PCA) and metabolic pathway elucidation. Results showed that probenazole application significantly affected the metabolic profile of rice seedlings, and the effect was proportionally leveraged with the increase of probenazole concentration. Probenazole resulted in a change of 54 metabolites. Salicylic acid, γ-aminobutyrate, shikimate and several other primary metabolites related to plant resistance were significantly up-regulated and some metabolites such as phenylalanine, valine and proline were down-regulated in probenazole-treated seedlings. These results revealed a metabolic pathway of rice seedlings induced by probenazole treatment regarding the resistance to M. grisea infection