Patent Publication Number: US-10769500-B2

Title: Localization-aware active learning for object detection

Description:
FIELD 
     The present disclosure relates generally to systems and methods for computer vision, and more particularly to applying active learning for object detection using an object detector that considers both localization and classification of an object in an image. 
     BACKGROUND 
     Conventional vision detection approaches have shown that with a large amount of annotated data, convolutional neural networks (CNNs) can be trained to achieve a super-human performance for various visual recognition tasks. However, these conventional vision detection methods have failed to investigate into effective approaches for data annotation, since data annotation is essential and expensive. For example, data annotation is especially expensive for object detection tasks. Compared to annotating image classes, which can be done via a multiple-choice question, annotating object location requires a human annotator to specify a bounding box for an object. Simply dragging a tight bounding box to enclose an object can cost 10-times more time than answering a multiple-choice question. Consequently, a higher pay rate has to be paid to a human labeler for annotating images for an object detection task. In addition to the cost, which is more difficult to monitor and control is the annotation quality. 
     Accordingly, there is need to achieve better performance with less annotation processes and, hence, less annotation budgets, among other things. 
     SUMMARY 
     Embodiments of the present disclosure are directed to providing systems and methods for applying active learning for object detection using an object detector that considers both localization and classification of an object in an image. 
     The present disclosure is based on the recognition that active learning that uses an uncertainty measure of features of objects in an image of a scene, can be an effective approach for annotating data for an image-classification and an image-localization task. Specifically, our realization is based on using a detection metric for active learning for object detection that includes a combination of an uncertainty of the object detector about a semantic class of an object in the image (classification) and an uncertainty of the object detector about a location of an object in the image (localization). However, coining to this realization required a further understanding through experimentation as to what can serve as a metric of uncertainty for object localization. 
     Specifically, through experimentation we learned using active learning that it is a machine learning procedure useful in reducing an amount of annotated data required to achieve a target performance specific to the aspects of the present disclosure. That, active learning can be applied to various computer-vision problems including object classification and activity recognition. Further, active learning can be used for initial training for a baseline model (classifier) with a small, labeled dataset, wherein the classifier can be applied to the unlabeled data. Such that, for each unlabeled sample, the active learning can estimate whether this sample contains critical information that has not been learned by the baseline model. Thus, once having identified the samples that bring the most critical information and are labeled by human annotators, they can be added to the initial training dataset to retrain the model. When comparing our method(s) to some conventional passive learning methods, which randomly selects samples from the unlabeled dataset, we discovered that our active learning can achieve the same accuracies as some conventional passive learning methods, however, with fewer but more informative labeled samples which are beneficial for many aspects of the present disclosure. 
     Our experimentation further led us to understand what can serve as a metric of uncertainty for object localization. We realized that the present disclosure algorithm(s) can be based on two quantitative measurements of the localization uncertainty. The first quantitative measurement of the localization uncertainty, includes a Localization Tightness (LT) metric that can estimate how tight the detected bounding boxes can enclose true objects. For example, the tighter the bounding box, the more certain localization. Localization tightness can be estimated based on an amount of adaptation of the initial bounding box, e.g., proposed by region proposal network (RPN), by an object detector. The second quantitative measurement of the localization uncertainty includes a Localization Stability (LS) metric based on whether the detected bounding boxes are sensitive to changes in the input image. Wherein to evaluate the localization stability, the present disclosure algorithm adds different amounts of Gaussian noise to pixel values of the image, and measures how the detected regions vary with respect to the noise. We note that at least one aspect of the present disclosure is that we realized, given an image, we can manipulate the image by adding a noise and measure hose the bounding box varies. Such that, this metric can be applied to all kinds of object detectors, especially those that do not have an explicit proposal stage. Also, that this metric can be applied to applied to video surveillance analysis, obstacle detection and path planning for autonomous driving, etc. 
     In other words, the present disclosure can analyze measurements on multiple public benchmarks and different object detectors. Which is unique and has innovative aspects, including being able to present different measurements to quantitatively evaluate the localization uncertainty of an object detector. Further that our measurements consider different aspects of object detection in spite that the ground truth of object locations is unknown, making our measurement suited for active learning. Another unique and innovative aspect includes demonstrating that in order to apply active learning for object detection, both the localization and the classification of a detector should be considered when sampling informative images. For example, the systems and methods of the present disclosure can train a detector on PASCAL 2012 benchmark, by non-limiting example, that achieves the same performance of conventional passive learning approaches, but with significantly less of annotated data. These performance levels can be achieved, by non-limiting example, by extending the method works for the classification with localization tightness (LT) and localization stability (LS), respectively, among other things. In other words, if the object detector models involve the adjustment of bounding box&#39;s size and location, we can further estimate the tightness. 
     To better explain the active learning systems and methods of the present disclosure, we provide some process flow steps for at least one method. For example, the method includes an object detector initially taking an image as input, and outputs a set of rectangular bounding boxes. Wherein each bounding box has a location and a scale of its (bounding box) shape, and a probability mass function of all classes. At least one training process step initially begins with a small training set of annotated images to train a baseline object detector. In order to improve the object detector by training with more images, the method continues to collect more images to annotate. Along with annotating all newly collected images, based on different characteristics of the current detector, we also select a subset of them for human annotators to label. Once having been annotated, these selected images can be added to the training set to train a new detector at some later time. Wherein the entire process can continue to collect more images, select a subset with respect to the new detector, annotate the selected ones with humans, re-train the detector and so on and so on. Wherein, we call such a cycle of data collection, selection, annotation, and training as a single round. 
     At least one key component of the present disclosure, among many key components, is in the selection step, which selects a set of informative images. The informativeness of an image is quantified as an uncertainty score, which specifies how uncertain a detector is toward its labels including the object class and location. An object in an image is specified as a bounding box. If an object bounding box has a high uncertainty score, then the image should be selected. Regarding computing the score, we consider the uncertainty in both classification and localization. The classification uncertainty of a bounding box can be similar to an active learning approach, which is based on the probability mass function of classes. For example, if the probability on a single class is close to 1.0, i.e. meaning that the probabilities for other classes are low, then, the detector is highly certain about its class. In contrast, when multiple classes have similar probabilities, each probability will be low because the sum of probabilities of all classes must be one. Thus, we can use one minus the maximum value of classes&#39; probability mass function as the classification uncertainty. 
     Further, given an image, we assign a score based on either the inconsistency between the localization and classification, or the stability of detected object locations. Then the unlabeled image with highest score can be sent to human labelers to select the boxes that contain objects and the corresponding object class. These images with their labels can be used to train the initial object detector to improve its performance. This process can be repeated till the performance of the object detector is satisfied. 
     According to an embodiment of the present disclosure, an active learning system that includes an input interface for receiving a set of images of a scene from a sensor. For example, the sensor(s) may be a video camera or camera like device, that obtains data from the scene including a set of images. The data may also include environmental data such as environmental conditions such as temperature, and the like. Further, a memory can be utilized to store active learning data that includes an object detector trained for detecting objects in images. A processor(s) in communication with the input interface and the memory, can be configured to detect a semantic class and a location of at least one object in an image selected from the set of images using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image (classification) and an uncertainty of the object detector about the location of the object in the image (localization). Finally, using an output interface, i.e. display type device, in communication with the processor, to display the image for human labeling when the detection metric is above a threshold. 
     According to another embodiment of the present disclosure, an active learning system including a memory that receives imaging data. The imaging data includes sets of images of a scene from a sensor via an input interface. Further, the memory includes a storage device with stored active learning data that includes an object detector trained for detecting objects in images. A processor is configured to connect to the memory, the input interface and an output interface. Wherein the processor executes instructions for producing a detection metric using the object detector. Wherein the object detector performs the steps of detecting a semantic class and a location of at least one object in an image selected from at least one set of images of the sets of images using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image and an uncertainty of the object detector about the location of the object in the image. Outputting the image using the output interface to an imaging interface connected to an imaging device, for displaying the image on the imaging device for human labeling when the detection metric is above a threshold. 
     According to another embodiment of the present disclosure, an active learning method for object detection using an object detector that considers both localization and classification of an object in an image. The method including receiving imaging data that includes sets of images of a scene from a sensor via an input interface and storing the imaging data in a memory. Wherein the memory includes a storage device having stored active learning data that includes an object detector trained for detecting objects in images. Using a processor in communication with the input interface and the memory. The processor is configured for executing instructions for producing a detection metric using the object detector. Wherein the object detector performs the steps of detecting a semantic class and a location of at least one object in an image selected from at least one set of images of the sets of images using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image and an uncertainty of the object detector about the location of the object in the image. Outputting the image via an output interface to an imaging device, to display the image for human labeling when the detection metric is above a threshold. Wherein the processor is in communication with the output interface and imaging device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The presently disclosed embodiments will be further explained with reference to the attached drawings. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the presently disclosed embodiments. 
         FIG. 1A  is a block diagram illustrating an active learning system, according to an embodiment of the present disclosure; 
         FIG. 1B  is a block diagram illustrating the active learning system of  FIG. 1A , that further includes some components used for the active learning system, according some embodiments of the present disclosure; 
         FIG. 1C  is a block diagram illustrating the active learning system of  FIG. 1A , in particular, the steps taken during object detection step, according some embodiments of the present disclosure; 
         FIG. 2A  is a block diagram of the data flow of an active learning system for training a neural network, according some embodiments of the present disclosure; 
         FIG. 2B  is a flowchart of an active learning system for training a neural network, according some embodiments of the present disclosure; 
         FIG. 3  is a block diagram of an active method for ranking the classification uncertainty and the importance of unlabeled images of  FIG. 2A  and  FIG. 2B , according some embodiments of the present disclosure; 
         FIG. 4  is a block diagram of an active learning system for annotating the unlabeled images, according some embodiments of the present disclosure; 
         FIG. 5  is a block diagram illustrating the labeling interface according some embodiments of the present disclosure; and 
         FIG. 6  is a block diagram of illustrating the active learning method of  FIG. 1A , that can be implemented using an alternate computer or processor, according to embodiments of the present disclosure. 
     
    
    
     While the above-identified drawings set forth presently disclosed embodiments, other embodiments are also contemplated, as noted in the discussion. This disclosure presents illustrative embodiments by way of representation and not limitation. Numerous other modifications and embodiments can be devised by those skilled in the art which fall within the scope and spirit of the principles of the presently disclosed embodiments. 
     DETAILED DESCRIPTION 
     The following description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the following description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing one or more exemplary embodiments. Contemplated are various changes that may be made in the function and arrangement of elements without departing from the spirit and scope of the subject matter disclosed as set forth in the appended claims of the present disclosure. 
     Specific details are given in the following description to provide a thorough understanding of the embodiments. However, understood by one of ordinary skill in the art can be that the embodiments may be practiced without these specific details. For example, systems, processes, and other elements in the subject matter disclosed may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known processes, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. Further, like reference numbers and designations in the various drawings indicated like elements. 
     Also, individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process may be terminated when its operations are completed, but may have additional steps not discussed or included in a figure. Furthermore, not all operations in any particularly described process may occur in all embodiments. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, the function&#39;s termination can correspond to a return of the function to the calling function or the main function. 
     Furthermore, embodiments of the subject matter disclosed may be implemented, at least in part, either manually or automatically. Manual or automatic implementations may be executed, or at least assisted, through the use of machines, hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium. A processor(s) may perform the necessary tasks. 
     Overview 
     Embodiments of the present disclosure are directed to systems and methods for applying active learning for object detection using an object detector that considers both localization and classification of an object in an image. 
     The present disclosure is based on the recognition that active learning that uses an uncertainty measure of features of objects in an image of a scene, can be an effective approach for annotating data for an image-classification and an image-localization task. Specifically, our realization is based on using a detection metric for active learning for object detection that includes a combination of an uncertainty of the object detector about a semantic class of an object in the image (classification) and an uncertainty of the object detector about a location of an object in the image (localization). However, coining to this realization required a further understanding through experimentation as to what can serve as a metric of uncertainty for object localization. 
     Specifically, we know through experimentation that active learning can be used for initial training for a baseline model (classifier) with a small, labeled dataset, wherein the classifier can be applied to the unlabeled data. Such that, for each unlabeled sample, the active learning can estimate whether this sample contains critical information that has not been learned by the baseline model. Thus, once having identified the samples that bring the most critical information and are labeled by human annotators, they can be added to the initial training dataset to retrain the model. 
     Further still, the active learning features of the present disclosure address the technological needs required to meet the demands of today&#39;s technological applications regarding visual feature description for fast inference methods in the context of autonomous driving applications, along with other applications. Specifically, the present disclosure is able to overcome the data of conventional autonomous driving applications/approaches. Because the scenarios of driving is complicated, ideally, the collected data for driving should have enough volume and cover a wide range of driving scenes. However, annotating all driving images can be extremely expensive. Via active learning, we can reduce the numbers of images to label, which is crucial for applications that require a very large scale of labeled images. 
     We realized through experimentation what can serve as a metric of uncertainty for object localization, that the present disclosure algorithm(s) can be based on two quantitative measurements of the localization uncertainty. The first quantitative measurement of the localization uncertainty, includes a Localization Tightness (LT) metric that can estimate how tight the detected bounding boxes can enclose true objects. For example, the tighter the bounding box, the more certain localization. Localization tightness can be estimated based on an amount of adaptation of the initial bounding box, e.g., proposed by region proposal network (RPN), by an object detector. The second quantitative measurement of the localization uncertainty includes a Localization Stability (LS) metric based on whether the detected bounding boxes are sensitive to changes in the input image. Wherein to evaluate the localization stability, the present disclosure algorithm adds different amounts of Gaussian noise to pixel values of the image, and measures how the detected regions vary with respect to the noise. Such that, this metric can be applied to all kinds of object detectors, especially those that do not have an explicit proposal stage. Also, that this metric can be applied to applied to video surveillance analysis, obstacle detection and path planning for autonomous driving, etc. 
     In other words, the present disclosure can analyze measurements on multiple public benchmarks and different object detectors. Which is unique and has innovative aspects, including being able to present different measurements to quantitatively evaluate the localization uncertainty of an object detector. Further that our measurements consider different aspects of object detection in spite that the ground truth of object locations is unknown, making our measurement suited for active learning. Another unique and innovative aspect includes demonstrating that in order to apply active learning for object detection, both the localization and the classification of a detector should be considered when sampling informative images. For example, the systems and methods of the present disclosure can train a detector on PASCAL 2012 benchmark, by non-limiting example, that achieves the same performance of conventional passive learning approaches, but with significantly less of annotated data, i.e. by about 15 to about 20%, and about 20 to about 25% less of annotated data. These performance levels can be achieved, by non-limiting example, by extending the method works for the classification with localization tightness (LT) and localization stability (LS), respectively, among other things. 
     The active learning system for some embodiments of the present disclosure can include a human machine interface, a storage device including neural networks, a memory, a network interface controller connectable with a network being outside the system. The active learning system can further include an imaging interface connectable with an imaging device, a processor configured to connect to the human machine interface, the storage device, the memory, the network interface controller and the imaging interface, wherein the processor can execute instructions for producing a detection metric using the object detector stored in the storage device. The object detector can perform steps that at least include detecting a semantic class and a location of at least one object in an image selected from at least one set of images of the sets of images. Further, the object detector performs the step of using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image and an uncertainty of the object detector about the location of the object in the image. Further still, the object detector performs the step of outputting the image to the imaging device, to display the image for human labeling when the detection metric is above a threshold. 
     To better explain the active learning systems and methods of the present disclosure, we provide some process flow steps for at least one method. For example, the method includes an object detector initially taking an image as input, and outputs a set of rectangular bounding boxes. Wherein each bounding box has a location and a scale of its (bounding box) shape, and a probability mass function of all classes. At least one training process step initially begins with a small training set of annotated images to train a baseline object detector. In order to improve the object detector by training with more images, the method continues to collect more images to annotate. Along with annotating all newly collected images, based on different characteristics of the current detector, we also select a subset of them for human annotators to label. Once having been annotated, these selected images can be added to the training set to train a new detector at some later time. Wherein the entire process can continue to collect more images, select a subset with respect to the new detector, annotate the selected ones with humans, re-train the detector and so on and so on. Wherein, we call such a cycle of data collection, selection, annotation, and training as a single round. 
     At least one key component of the present disclosure, among many key components, is in the selection step, which selects a set of informative images. The informativeness of an image is quantified as an uncertainty score, which specifies how uncertain a detector is toward its labels including the object class and location. An object in an image is specified as a bounding box. If an object bounding box has a high uncertainty score, then the image should be selected. Regarding computing the score, we consider the uncertainty in both classification and localization. The classification uncertainty of a bounding box can be similar to an active learning approach, which is based on the probability mass function of classes. For example, if the probability on a single class is close to 1.0, i.e. meaning that the probabilities for other classes are low, then, the detector is highly certain about its class. In contrast, when multiple classes have similar probabilities, each probability will be low because the sum of probabilities of all classes must be one. Thus, we can use one minus the maximum value of classes&#39; probability mass function as the classification uncertainty. 
       FIG. 1A  is a block diagram illustrating an active learning system, according to an embodiment of the present disclosure. Initially, the active learning system  100  includes the step of acquiring a set of images  120  of a scene from a sensor via an input interface by a processor, wherein the set of images includes objects. The next step includes using the processor to input the acquired set of images into an object detector  125  stored in memory. The object detector step  130  detects a semantic class and a location of at least one object in an image selected from the set of images. For each image, step  131  computes a metric as the combination of an uncertainty of the object detector about the semantic class of the object in the image, and an uncertainty of the object detector about the location of the object in the image. The next step  135  includes using an output interface to display the image for human labeling when the detection metric is above a threshold. 
       FIG. 1B  is a block diagram illustrating the active learning system of  FIG. 1A , that further includes some components used for the active learning system, according some embodiments of the present disclosure. The active learning system  100  includes a scene  110  that provides input images obtain by a sensor device  112 . For example, the sensor(s)  112  may be a video camera or camera like device, that obtains data from the scene  110  so as to obtain the set of images  114 . The data may also include environmental data such as environmental conditions such as temperature, and the like. The input images from the scene  110  include set of images  114  that are obtained by an input interface  116  that is processed by a processor  140 . It is contemplated the set of images  114  can be stored in a memory  145  and then later processed by the processor  140 . Further, the input interface  116  and the memory  145  may be in communication with a computer  150 . Further still, the processor  140  processes the set of images  114  using an object detector  142  that can be stored in the memory  145 . 
     The object detector  142  is processed by the processor(s)  140 , such that the object detector  142  can be configured to detect a semantic class and a location of at least one object in an image. For each image, it computes a detection metric based on the classes and locations of the detected object. Step  144  selects image with detection metrics higher than a threshold. Finally, an output interface  195 , i.e. display type device, in communication with the processor  145 , can be utilized to display the image with the selected images. 
       FIG. 1C  is a block diagram illustrating the active learning system of  FIG. 1A , in particular, some steps taken during the object detection step  130  of  FIG. 1A  and object detection step  142  of  FIG. 1B , according some embodiments of the present disclosure. Given an image ( 161 ), it is first applied to object detector ( 162 ) to measure the classification uncertainty ( 165 ). If this object detector also contains information about the initial bounding box, block  164  compares the initial bounding box and the final one to measure the tightness ( 167 ). To measure the localization stability, we add noise ( 162 ) to the image ( 161 ) and apply the same object detector ( 160 , which is identical to  163 ). Step  166  compares the detected boxes generated by detector  163 , which is based on the original image, and the detected boxes from detector  1620 , which use the same detector algorithm but with noise in the image. Step  166  outputs the localization stability, which is combined with the classification uncertainty from block  165 , localization tightness of  167  if exists, to form the final uncertainty measurement for the input image ( 161 ). 
     For example, the object detector can detect the semantic class of the at least one object in the image (classification) based on numerous machine learning algorithms such as convolution neural networks, support vector machine, etc. Given a classification algorithm, it is applied to the image object to calculate the probability of this object to be each of the pre-defined classes. Such that, the uncertainty of the object detector about the semantic class of the object in the image can be a function of these probabilities of all pre-defined classes. For example, the more classes having a high probability, then there is a less certainty of estimating the classified class. 
     Further, the object detector can detect the location of the at least one object in the image (localization). To detect the objects, multiple boxes of different scales and aspect ratios are generated over the images. For each box, the similarity from image pixels within to pre-collected patterns of objects of interest (e.g. vehicles, pedestrians, trucks, etc.) is measured. The boxes can be further adjusted to fit the pattern as much as possible. As several boxes can overlap the same object, we can further filter the boxes with highest similarity among others that overlap with it. Although the object detector might adjust the initial box to fir the closest and most possible object, the box can be still loose. As a result, the uncertainty of the object detector about the location of the object in the image is a function of a tightness of a bounding box around the object. Wherein the detection metric can be proportional to inconsistency between certainties of the objector detector about the semantic class of the object and the tightness of the location of the object. Further, the function of the tightness of the bounding box around the object can be determined using a ratio of an intersection of an initial bounding box and a final bounding box to a union of the initial bounding box and the final bounding box. It is possible that the object detector can estimate an initial bounding box surrounding the object and adjust the initial bounding box to produce the final bounding box. Further, it is contemplated that the initial bounding box can be proposed by a region proposal network. Wherein the object detector places a plurality of initial bounding boxes at different locations in the image and selects the initial bounding box with a maximal inclusion of an object of a specific semantic class. 
     We note that the uncertainty of the object detector about the location of the object can be a function of a stability of a bounding box around the object. Wherein the processor can detect the object in the image modified with noise and determines the stability of the bounding box based on a difference between the bounding box determined for the image and the bounding box determined for the image modified with noise. Further, that the detection metric can be a weighted sum of the uncertainty of the neural network about the class of the object in the bounding box and the stability of the bounding box is based on how much its location and size changes with respect to the added noise. If the location and size can be closed under different degree of noise, this bounding box is said to be stable. 
     According to some embodiments of the present disclosure, an active learning system includes a human machine interface, a storage device including neural networks, a memory, a network interface controller connectable with a network being outside the system. The active learning system further includes an imaging interface connectable with an imaging device, a processor configured to connect to the human machine interface, the storage device, the memory, the network interface controller and the imaging interface, wherein the processor executes instructions for classifying an object in an image using the neural networks stored in the storage device, in which the neural networks perform steps of determining features of a signal using the neuron network, determining an uncertainty measure of the features for classifying the signal, reconstructing the signal from the features using a decoder neuron network to produce a reconstructed signal, comparing the reconstructed signal with the signal to produce a reconstruction error, combining the uncertainty measure with the reconstruction error to produce a rank of the signal for a necessity of a manual labeling, labeling the signal according to the rank to produce the labeled signal, and training the neuron network and the decoder neuron network using the labeled signal. 
       FIG. 2A  is a block diagram of the data flow of an active learning system for training a neural network, according some embodiments of the present disclosure. For example, an initial setting of the active learning system  200  includes a neural network  210  initialized with random parameters, an initial set of labeled training images  201 , a trainer  202 , a set of unlabeled images  203 . In this case, the neural network  210  is a user defined neural network. 
     The active learning system  200  attempts to efficiently query the unlabeled images for performing annotations through the basic workflow shown in  FIG. 2A . Based on the neural network (NN)  210  with randomly initialized parameters, the trainer  202  updates network parameters by fitting the NN  210  to the initial labeled training dataset of images  201 . As a result, a trained NN  220  with the updated network parameters is used to rank the importance of images in an unlabeled dataset  203 . The unlabeled images  203  are sorted according to importance scores  204  obtained from a ranking result performed by the trained NN  220 . The K most important images  205  are stored into a labeling storage in a memory (not shown in  FIG. 2A ) associated to a labeling interface  206 . In response to data inputs made by an operator (or annotator), the labeling interface  206  generates annotated images  207  having the ground truth labels. These annotated images  207  are then added to the initial labeled training dataset  201  to form a new training dataset  208 . The trainer  202  then retrains the network  220  by fitting the new training dataset of images  208  and obtains updated neural network parameters  230 . This procedure is iterative. The updated neural network parameters  230  are used to rank the importance of the rest of the unlabeled images  203 , and the K most important images  205  are sent to the labeling interface  206 . Usually, this procedure is repeated several times until a predetermined preferred performance is achieved or the budget for annotations is empty. 
     Further, still referring to  FIG. 2A , the determining features may be performed by using an encoder neural network. In this case, the encoder neural network can perform feature analysis of given signals. In some cases, the signal may be an electroencephalogram (EEG) or an electrocardiogram (ECG). The neural network can use biological signals instead of image signals. Accordingly, some embodiments of the present disclosure can be applied to provide specific signals for assisting a diagnosis of medical doctors. 
       FIG. 2B  is a flowchart of an active learning system for training a neural network, according some embodiments of the present disclosure. For example, the active learning system  200  attempts to efficiently query the unlabeled images for the annotation through a process flow shown in the figure. The process flow of  FIG. 2B  can include the following stages: 
     S 1 —An initial labeled training dataset is provided and the neural network is trained by using the dataset. 
     S 2 —By using the trained NN obtained in step S 1 , each image in the unlabeled dataset is evaluated and a score would be assigned to each image. 
     S 3 —Given the score obtained in step S 2 , images with the top K highest scores are selected for labeling by the annotation device. 
     S 4 —The selected images with newly annotated labels are added into the current (latest) labeled training set to get a new training dataset. 
     S 5 —The network is refined or retrained based on the new training dataset. 
       FIG. 2B  shows the active learning algorithms of the active learning system  200  attempt to efficiently query images for labeling images. An initialization model is trained on an initial for small labeled training set. Based on the current model, which is the latest trained model, the active learning system  200  tries to find the most informative unlabeled images to be annotated. A subset of the informative images are labeled and added to the training set for the next round of training. This training process is iteratively performed, and the active learning system  200  carefully adds more labeled images for gradually increasing the accuracy performance of the model on the test dataset. By the very nature, the algorithms of the active learning system  200  usually work much better than the standard approach for training, because the standard approach simply selects the samples at random for labeling. 
     Although a term “image” is used in the specification, another “signal” can be used in the active learning system  200 . For instance, the active learning system  200  may process other signals, such as an electroencephalogram (EEG) or an electrocardiogram (ECG). Instead of the images, the EEG or ECG signals can be trained in the active learning system  200 . Then the trained active learning system  200  can be applied to determine or judge abnormality with respect to an input signal, which can be a useful assistance for medical diagnosis of relevant symptoms. 
       FIG. 3  shows a block diagram of process steps for ranking the importance of unlabeled images in an active learning system of  FIG. 2A  and  FIG. 2B , according to some embodiments of the present disclosure. When an input image from the set of unlabeled images  203  of  FIG. 2A , is provided to a front end of the NN  220  of  FIG. 2A , in step  302 , the trained NN  220  generates features  303  and outputs a classification result via a softmax output layer  304 . The classification result is used for calculating the importance score  204  of the input signal through uncertainty measure  305  based on the Rényi entropy. 
     The trained NN  220  of  FIG. 2A  is used for extracting the features  303  for each of the images in the unlabeled dataset  203  and also for computing classifications by the softmax output layer  304 . The classification result obtained by the softmax output layer  304  is a probability vector of dimension D where the dimension D is the number of object classes. Denoting the input image by x and the classification result computed by the softmax output layer  304  indicating a probability vector by p, each dimension of the probability vector p represents the probability that the input image  203  belongs to a specific class. The sum of the components of p is equal to one. The uncertainty of the class of the input image can then be measured in the step of the uncertain measure  305  by an entropy function H(x). When the entropy H(x) is computed based on the Shannon entropy, the uncertainty of the class of the input image is given by
 
 H ( x )=Σ i=1   D   −p   i  log  p   i   (1)
 
     Still referring to  FIG. 3 , in an uncertainty method, the uncertainty measure can be used as the importance score of the unlabeled image  204 . Further, other entropy measures defined in the Renyi entropy category can be used for the uncertainty computation. For instance, the entropy function H(x) may be Collision entropy, H(x)=−log Σ i=1   D p i   2  or Min-entropy, 
     
       
         
           
             
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                     log 
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                     ⁢ 
                     max 
                     ⁢ 
                     
                         
                     
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                     pi 
                   
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     Further, entropy based methods may be defined by 
               H   ⁡     (   x   )       =     1   -       log   ⁢           ⁢   max   ⁢           ⁢   pi     i             
for obtaining an estimate of uncertainty, and an experimental result is shown in  FIG. 5 .
 
     Still referring to  FIG. 3 , since the uncertainty method is a universal active learning method, it can be used in conjunction with various classifiers (SVMs, Gaussian processes, or neural networks) as long as the vector representing the class probability can be derived from each input image. In this case, the uncertainty method does not utilize the property of the classifier and reaches sub-optimal performance. 
       FIG. 4  shows a block diagram of an active learning system  400  according to some embodiments of the present disclosure. The active learning system  400  can include a human machine interface (HMI)  410  connectable with a keyboard  411  and a pointing device/medium  412 , a processor  420 , a storage device  430 , a memory  440 , a network interface controller  450  (NIC) connectable with a network  490  including local area networks and internet network, a display interface  460 , an imaging interface  470  connectable with an imaging device  475 , a printer interface  480  connectable with a printing device  485 . The processor  420  may include one or more than one central processing unit (CPU). The active learning system  400  can receive electric text/imaging documents  495  via the network  490  connected to the NIC  450 . The active learning system  400  can receive annotation data from the annotation device  413  via the HMI  410 . Further, the annotation device  413  can include a display screen, and the display screen of the annotation device  413  can be configured to display the labeling interface  206  of  FIG. 2A  that allows the operator to perform labeling process of unlabeled images stored in the memory  440  by showing the unlabeled image in the display region  401  with the selection area  402  having predetermined annotation boxes and predetermined labeling candidates to be selected. 
     The storage device  430  includes original images  431 , a filter system module  432 , and a neural network  400 . For instance, the processor  420  loads the code of the neural network  400  in the storage  430  to the memory  440  and executes the instructions of the code for implementing the active learning. Further, the pointing device/medium  412  may include modules that read programs stored on a computer readable recording medium. 
     Referring to  FIG. 4  and  FIG. 5 ,  FIG. 5  shows an example of the labeling interface  106  of  FIG. 2A  according to some embodiments of the present disclosure. In  FIG. 5 , the labeling interface  206  of  FIG. 2A  includes a display region  501  and a selection area  502 . The labeling interface  206  of  FIG. 2A  can be installed in the annotation device  413  of  FIG. 4 , which indicates the labeling interface  206  of  FIG. 2A  on a display of the annotation device  413  of  FIG. 4 . In some cases, the labeling interface  206  of  FIG. 2A  can be installed by an input/output interface (not shown in the  FIG. 5 ) connectable to the human machine interface (HMI)  410  of  FIG. 4  via the network  490  of  FIG. 4 . When the labeling interface  206  of  FIG. 2A  receives an unlabeled image of the K most important unlabeled images  205  in step S 6  of  FIG. 2A , the labeling interface  206  of  FIG. 2A  shows the unlabeled image on the display region  501  of  FIG. 5 . The selection area  502  of  FIG. 5  indicates predetermined candidates for labeling the unlabeled image shown on the display region  501 . The labeling interface  206  of  FIG. 2A  allows an operator to assign one of selectable annotations indicated in the selection area  502  with respect to the unlabeled image shown in the display region  501 . In  FIG. 5 , the selection area  502  provides selection boxes with predetermined labeling candidates: Dog, Cat, Car, and Plane. As an example,  FIG. 5  shows an unlabeled image indicating a cat image  503  displayed in the display region  501 . In this case, the annotation box of Cat is checked by the operator (annotator) in response to the cat image shown in the selection area  502 . Besides the categories, the labeler should also draw a bounding box that can tightly around the object. In this example, the box  504  is a tight bounding box to enclose the cat. The labeling interface  206  of  FIG. 2A  is configured to load and show unlabeled images stored the labeling storage in the memory according to the operations by the operator. The images labeled by the labeling interface  206  of  FIG. 2A  are stored into a new training image storage area in the memory in step S 3  as newly labeled training images  207  as seen in  FIG. 2A . 
     Features 
     The object detector can detect the location of the at least one object in the image, at least by generating multiple boxes of different scales and aspect ratios over each image for the set of the images. Comparing for each box, pixels within each image in the box to stored patterns of objects of interest of the active learning data in memory, to determine a measurement of similarity for each box of the multiple boxes. Adjusting a location of each box to fit at least one stored pattern of objects, if one or more box overlaps a same stored object pattern. Filtering some of the boxes having the determined similarity measurement amounts above a predetermined overlap threshold from other boxes having the determined measurement similarity amount that overlap. 
     It is possible the object detector can detect the semantic class of the at least one object in the image at least by comparing for each box, determined patterns within each image in the box to stored image patterns of different semantic classes of the active learning data, to determine a measurement of similarity for each box of the multiple boxes. Wherein the determined patterns within the image in the box is composed of smaller image patterns that are defined image features of different scale/colors/textures in the image selected from the set of images. Normalizing all the classes, based on the determined measured similarities to all classes, such that a sum of the normalized similarity equal 1, and the normalized similarity to each class becomes the class probability. 
     Further, it is possible that the uncertainty of the object detector about the location of the object in the image can be a function of a tightness of a bounding box around the object. At least one aspect can include the detection metric can be proportional to an inconsistency between certainties of the objector detector about the semantic class of the object and the tightness of the location of the object. At least another aspect can include the function of the tightness of the bounding box around the object is determined using a ratio of an intersection of an initial bounding box and a final bounding box to a union of the initial bounding box and the final bounding box. Wherein the object detector estimates an initial bounding box surrounding the object and adjusts the initial bounding box to produce the final bounding box. Such that, the initial bounding box is specified by users or refined via a region proposal network, and the object detector places a plurality of initial bounding boxes at different locations in the image and selects the initial bounding box with a maximal inclusion of an object of a specific semantic class. 
     It is contemplated the uncertainty of the object detector about the location of the object can be a function of a stability of a bounding box around the object. Wherein the processor detects the object in the image modified with noise and determines the stability of the bounding box based on a difference between the bounding box determined for the image and the bounding box determined for the image modified with noise. Wherein the detection metric is a weighted sum of the uncertainty of the neural network about the class of the object in the bounding box and the stability of the bounding box is based on how sensitive the bounding box under the noise; if the location and size of an object&#39;s bounding box can be close under different degree of noise, this bounding box is stable. Further, the output interface may be a display device. 
     Contemplated is that the active learning method for object detection uses an object detector that considers both localization and classification of an object in an image. Wherein once the inconsistency, (i.e. the detection metric is proportional to inconsistency between certainties of the objector detector about the semantic class of the object and the tightness of the location of the object), or stability of box, (i.e. the uncertainty of the object detector about the location of the object is a function of a stability of a bounding box around the object, and the processor detects the object in the image modified with noise and determines the stability of the bounding box based on a difference between the bounding box determined for the image and the bounding box determined for the image modified with noise), is computed as the score for each image, we can choose unlabeled image with highest inconsistency or least stability. Such that the chosen images will be sent to human labeler to annotate to select the boxes that contain object and the corresponding class. These images with their labels will be used to train the initial object detector to improve its performance. This process can be repeated till the performance of the object detector is satisfied. 
       FIG. 6  is a block diagram of illustrating the method of  FIG. 1A , that can be implemented using an alternate computer or processor, according to embodiments of the present disclosure. The computer  611  includes a processor  640 , computer readable memory  612 , storage  658  and user interface  649  with display  652  and keyboard  651 , which are connected through bus  656 . For example, the user interface  649  in communication with the processor  640  and the computer readable memory  612 , acquires and stores the measuring data in the computer readable memory  612  upon receiving an input from a surface, keyboard surface, of the user interface  657  by a user. 
     Contemplated is that the memory  612  can store instructions that are executable by the processor, historical data, and any data to that can be utilized by the methods and systems of the present disclosure. The processor  640  can be a single core processor, a multi-core processor, a computing cluster, or any number of other configurations. The processor  640  can be connected through a bus  656  to one or more input and output devices. The memory  612  can include random access memory (RAM), read only memory (ROM), flash memory, or any other suitable memory systems. 
     Still referring to  FIG. 6 , a storage device  658  can be adapted to store supplementary data and/or software modules used by the processor. For example, the storage device  658  can store historical device data and other related device data such as manuals for the devices, wherein the devices are sensing device capable of obtaining measured data as mentioned above regarding the present disclosure. Additionally, or alternatively, the storage device  658  can store historical data similar to the measuring data. The storage device  658  can include a hard drive, an optical drive, a thumb-drive, an array of drives, or any combinations thereof. 
     The system can be linked through the bus  656  optionally to a display interface (not shown) adapted to connect the system to a display device (not shown), wherein the display device can include a computer monitor, camera, television, projector, or mobile device, among others. 
     The computer  611  can include a power source  654 , depending upon the application the power source  654  may be optionally located outside of the computer  611 . A printer interface  659  can also be connected through bus  656  and adapted to connect to a printing device  632 , wherein the printing device  632  can include a liquid inkjet printer, solid ink printer, large-scale commercial printer, thermal printer, UV printer, or dye-sublimation printer, among others. A network interface controller (NIC)  634  is adapted to connect through the bus  656  to a network  636 , wherein measuring data or other data, among other things, can be displayed on a third party display device, third party imaging device, and/or third party printing device outside of the computer  611 . 
     Still referring to  FIG. 6 , the measuring data or other data, among other things, can be transmitted over a communication channel of the network  636 , and/or stored within the storage system  658  for storage and/or further processing. Further, the measuring data or other data may be received wirelessly or hard wired from a receiver  646  (or external receiver  638 ) or transmitted via a transmitter  647  (or external transmitter  639 ) wirelessly or hard wired, the receiver  646  and transmitter  647  are both connected through the bus  656 . The computer  611  may be connected via an input interface  608  to external sensing devices  644  and external input/output devices  641 . The computer  611  may be connected to other external computers  642  and external sensing devices  644 . An output interface  609  may be used to output the processed data from the processor  640 . Further, the sensors  604  can obtain the set of images from the scene  602 . 
     The above-described embodiments of the present disclosure can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. Use of ordinal terms such as “first,” “second,” in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements. 
     Although the present disclosure has been described with reference to certain preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the present disclosure. Therefore, it is the aspect of the append claims to cover all such variations and modifications as come within the true spirit and scope of the present disclosure.