ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING BASED SYSTEM FOR IMAGE RECOGNITION AND METHOD THEREOF

Abstract

The present invention discloses an Artificial Intelligence (AI) and Machine Learning (ML)-based system and method for image recognition. The system integrates an image capture device, preprocessing unit, data storage unit, feature extraction module utilizing convolutional neural networks (CNNs), and a classification module leveraging machine learning models such as support vector machines (SVMs) and deep learning algorithms. The system continuously improves through reinforcement learning, adapting to new image data for high accuracy and efficiency in real-time applications. Key features include hardware acceleration, scalable architecture, and real-time feedback for applications in autonomous driving, medical imaging, and surveillance. The system enhances processing speed, accuracy, and adaptability, overcoming limitations of prior art systems. This innovation supports diverse use cases by providing a robust, scalable, and secure solution for real-time image recognition in dynamic environments.

Specification

Description:[014] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[015] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[016] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[017] Also, it is noted that individual embodiments may be described as a process that 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 is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[018] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[019] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[020] In an embodiment of the invention and referring to Figures 1, the present invention provides an advanced system and method for image recognition, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to deliver high accuracy, efficiency, and adaptability in processing and analyzing image data. This invention integrates multiple components in a novel manner, optimizing hardware resources while using sophisticated software algorithms to handle real-time image recognition tasks in diverse applications.
[021] The image recognition system comprises several core hardware and software components working together to process, analyze, and classify images. These components include an image capture device (such as a camera or scanner), a processing unit (comprising central processing units or GPUs), machine learning modules, and a data storage unit. The system is designed to process large volumes of image data, classify images, detect objects, and adapt to new image datasets without requiring substantial manual intervention. The interconnection and interaction between these components ensure high-performance image recognition in real-time applications.
[022] The first step in the image recognition process is the capture of images. The system utilizes high-resolution cameras or imaging devices equipped with advanced sensors to capture images in various formats (e.g., RGB, grayscale). The captured images are then preprocessed to remove noise, enhance quality, and normalize image data. This preprocessing is essential for ensuring that the raw data is suitable for further analysis. Preprocessing tasks include techniques like histogram equalization, edge detection, and data augmentation, which improve the clarity of images before feeding them into the machine learning models.
[023] Once the images are captured and preprocessed, they are stored in a high-speed data storage unit, such as solid-state drives (SSDs) or cloud-based storage. The storage unit is designed to handle large volumes of image data and supports fast retrieval for further analysis. The data storage system is integrated with a robust data management framework, which ensures that images are organized efficiently and can be accessed or updated as required by the machine learning system. This setup supports large-scale operations and ensures that the system can handle vast datasets commonly encountered in fields such as medical imaging or autonomous vehicles.
[024] Feature extraction is a crucial stage in image recognition. The system uses convolutional neural networks (CNNs), which are deep learning algorithms, to extract significant features from the image data. CNNs are well-suited for image processing tasks due to their ability to identify spatial hierarchies and patterns in visual data. The CNN extracts features such as edges, textures, shapes, and objects within the image, which are then passed on to subsequent layers for classification or further analysis. The use of CNNs enhances the system's ability to recognize complex patterns and objects in images with high accuracy.
[025] The extracted features are fed into machine learning models that perform classification and recognition tasks. The invention integrates multiple machine learning techniques, including support vector machines (SVMs), decision trees, and deep learning models like long short-term memory (LSTM) networks. These algorithms are trained on vast datasets to recognize and classify objects, scenes, or other entities within an image. The system continuously improves its performance through supervised and unsupervised learning techniques, ensuring that it can adapt to new data or changing conditions.
[026] The system also implements a multi-class classification approach, allowing it to recognize various types of objects within a single image. The classification process is done in real-time, providing immediate feedback to the system, making it ideal for applications such as autonomous driving, where decisions must be made instantaneously.
[027] One of the novel aspects of the present invention is its ability to adapt to new datasets dynamically. The system uses a feedback loop that allows it to update its knowledge base based on new image data. This continuous learning mechanism is powered by reinforcement learning (RL) techniques, which enable the system to modify its algorithms in response to new, previously unseen images. As new data is processed, the system refines its models, ensuring that it maintains high accuracy over time. This adaptability is especially important for real-world applications where conditions frequently change.
[028] The interconnection between hardware and software components is critical to the overall performance of the image recognition system. The processing unit, which may consist of CPUs or GPUs, works in tandem with the image capture devices to ensure smooth data flow. The data storage unit is interconnected with the processing unit, ensuring that image data can be retrieved and processed quickly. The machine learning models are hosted on high-performance computing hardware, enabling fast model training and inference.
[029] Additionally, the entire system is networked, allowing for distributed processing and cloud-based training. This interconnection enables the system to scale, with multiple devices working together to process images in parallel, making it suitable for large-scale operations.
[030] A key feature of the present invention is its ability to process images in real-time. The system is optimized for speed, utilizing hardware accelerators such as GPUs or custom-designed application-specific integrated circuits (ASICs) to accelerate computation-intensive tasks like feature extraction and classification. These accelerators ensure that the system can handle large image datasets and perform complex calculations without delay. The result is a system capable of delivering immediate results, which is critical for applications such as surveillance, autonomous vehicles, or real-time medical imaging.
[031] The hardware components, such as image sensors, processing units, and storage, work seamlessly with the software components that include machine learning algorithms, data preprocessing units, and user interfaces. The software is optimized to make the best use of available hardware resources, ensuring that the system operates efficiently and with minimal power consumption. A modular software architecture is employed, allowing for easy updates to machine learning models and the integration of new hardware components as technology evolves.
[032] The image recognition system is designed to be scalable, capable of processing images from a single device or across a distributed network of devices. The system architecture supports horizontal scaling, allowing additional devices or servers to be added to handle increased workloads. The software is designed to be flexible, allowing it to integrate with a variety of hardware platforms, from edge devices to cloud-based infrastructures, depending on the application requirements. This scalability ensures that the system can be deployed in a wide range of environments, from small-scale systems to large enterprise solutions.
[033] The invention also incorporates robust error-handling mechanisms to ensure consistent performance. In case of hardware malfunctions or data inconsistencies, the system can automatically detect and correct errors without significant disruption. The system includes redundancy features to maintain functionality during component failure and prevent the loss of critical data. This robustness ensures that the system can be used in mission-critical applications without compromising reliability.
[034] As image recognition systems often handle sensitive data, the present invention includes built-in security measures to protect the integrity and privacy of the images being processed. The system supports encrypted communication between components, secure data storage, and user authentication mechanisms. These features ensure that data privacy is maintained and that the system adheres to industry standards for secure image processing.
[035] The method of operation of the image recognition system is as follows: First, images are captured by the input devices and preprocessed for quality enhancement. The preprocessed images are then stored in the storage unit. Feature extraction algorithms are applied to identify key patterns and objects within the images, which are then classified using machine learning models. The results are provided to the user or system interface in real-time, allowing for immediate action or decision-making. Throughout the process, the system continuously updates and improves its algorithms through continuous learning, ensuring ongoing optimization.
[036] To demonstrate the efficacy of the invention, the system was compared to existing image recognition technologies in terms of processing speed, accuracy, and adaptability. The results, shown in Table 1, indicate that the present invention outperforms traditional systems in each of these areas.

[037] The present invention provides a novel and efficient AI and ML-based system for image recognition, overcoming the limitations of prior art systems by offering faster processing, higher accuracy, and greater adaptability. The integration of advanced machine learning algorithms, hardware acceleration, and continuous learning mechanisms ensures that the system can meet the demands of real-time applications across a wide range of industries. The system's scalability, flexibility, and robustness make it a versatile solution for various use cases, setting it apart from traditional image recognition systems. , Claims:1. An Artificial Intelligence and Machine Learning-based system for image recognition, comprising:
a) an image capture device for capturing image data in a predetermined format;
b) a preprocessing unit configured to preprocess the captured image data by performing noise reduction, image enhancement, and data normalization to produce preprocessed image data;
c) a data storage unit configured to store the preprocessed image data and facilitate fast retrieval;
d) a feature extraction unit using convolutional neural networks (CNNs) for extracting significant features from the preprocessed image data;
e) a classification module utilizing at least one machine learning model selected from the group consisting of support vector machines (SVMs), decision trees, and deep learning models to classify the extracted features into predefined categories;
f) a reinforcement learning unit for adapting the system's machine learning models based on new image data, wherein the system continuously improves its performance through dynamic learning;
g) a processing unit comprising a central processing unit (CPU) or graphics processing unit (GPU) for processing the image data and executing the machine learning models;
h) a real-time feedback mechanism for enabling immediate processing and classification of image data for applications in autonomous vehicles, medical imaging, surveillance, and similar real-time systems.
2. The system as claimed in Claim 1, wherein the image capture device includes high-resolution cameras or imaging sensors capable of capturing images in multiple formats including but not limited to RGB and grayscale formats.
3. The system as claimed in Claim 1, wherein the feature extraction unit employs convolutional neural networks (CNNs) to identify spatial patterns, edges, shapes, and objects within the image, which are then used for further classification and recognition tasks.
4. The system as claimed in Claim 1, wherein the reinforcement learning unit updates the system's machine learning models by utilizing new, previously unseen image data, enabling continuous learning and adaptation to changing conditions.
5. The system as claimed in Claim 1, wherein the data storage unit is a high-speed solid-state drive (SSD) or cloud-based storage system, configured to handle large datasets and provide quick data retrieval for analysis and processing.
6. The system as claimed in Claim 1, wherein the classification module further incorporates deep learning models, including but not limited to Long Short-Term Memory (LSTM) networks, to improve accuracy in object recognition and classification in dynamic environments.
7. The system as claimed in Claim 1, wherein the real-time feedback mechanism provides immediate results upon classification of the image, enabling instantaneous decision-making in time-sensitive applications such as autonomous driving or medical diagnosis.
8. The system as claimed in Claim 1, further includes hardware accelerators such as GPUs or application-specific integrated circuits (ASICs) for accelerating computation-intensive tasks in feature extraction and classification.
9. The system as claimed in Claim 1, wherein the machine learning models are trained using large datasets and continually optimized through supervised, unsupervised, and reinforcement learning techniques to improve recognition accuracy and classification performance.
10. A method for image recognition comprising the steps of:
i. capturing image data using an image capture device;
ii. preprocessing the captured image data by applying noise reduction, enhancement, and normalization techniques;
iii. storing the preprocessed image data in a data storage unit;
iv. extracting features from the preprocessed image data using convolutional neural networks (CNNs);
v. classifying the extracted features into predefined categories using at least one machine learning model;
vi. adapting the machine learning models based on new image data using reinforcement learning techniques to continuously improve the performance of the system;
vii. providing real-time feedback of the classified image data for use in applications such as autonomous driving, medical imaging, and surveillance.

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