CN7023 Artificial Intelligence & Machine Vision Assignment Sample 2023

Abstract

Visual classification is the vast and important features considered with the object detection and extracting the features in computer vision. The workflow must be creative to work with the fashion department as the classification of garments may depend on the various features. This makes the different features based on the designer views and data experts that are provided with the overall production with the order for organizing the campaigns. The different techniques used with the visual classification with the image processing and machine learning approach. The designed project is implemented and developed based on the different approach that is described with the different collaboration. The various features were extracted with the classification based accuracy using the image recognition and deep learning techniques.

Introduction

The visual analysis is obtained with the various features that is used with production based on different strategies for fashion design with the workflow creation with the new product. The first step is to visualize the different outcome that is designed with the data collection and categorization. There are various teams that are employed with outsourcing all over the world. The designers comprises of the various image that is accessed with the visualization that is processed with the different types of the source data which is predicted with different characteristics. This image is classified with the different sources that are accessed with the various steps in visual analysis for analyzing, extracting and recognizing various features based on the final extraction of the data with the 3D image machine learning or deep learning approach. The proposed system consists of various deep learning frameworks that is recommended with the fashion clothes with the visuals image with the dataset. The input is considered as image that extracts the features based on the input image. The neural network is classified with the image based feature that extracts and drive the data based on other algorithm. The deep fashion dataset is used with the test recommendation system that is proposed with the framework based on instance and performance with visual based approach.

The fashion products were categorized and analyzed with the different team to extract the knowledge with the error free zone which is provided with the wrong result. The number of articles was required with the Adidas team based on different articles with the attributes and other categories based on the classification. The subset is provided with the seven attributes with various features like size and type of log, shape of neck, sleeve shape, color palette, material used, pattern and print. This is provided with the identification of the garment with similar product. Online shopping is the rising peak with the e-marketer that provides the fast growth with the fashion with double the retail sales that account with the recommended visualization techniques used with the classification of the image or object that may be described with consumer. The content based filtering technique is provided with different approach that uses the historical data with collaborative filtering method. The convolutional neural network is provided with the language processing system for recognition of the image with the visual based approach. The classification, recommendation and functionalities were used with convolutional neural network is used with the image recognition based on the proposed system that is mainly focused on the neural network which is used with the convolutional layer that is classified with the proposed work.

There are different steps involved with the fashion architecture design that may explain the different layer for classifying the image based on the sampling method. The core automation process is classified with the process increases the classification using the machine learning process based on classification that uses segmentation with deep learning techniques. These algorithms were used with the image recognition technique that automates the object with the automatization with the computer vision with machine learning techniques. The input is provided with the convolutional layer that is connected with the fashion classification based on different layers. This input holds the pixel value with the height and weight based on result that is unchanged with the sampling method.

This article is structured as techniques used with the deep learning method and convolutional neural network method in section 2, the simulation method used with this approach is explained with the section 3. The experimental method is explained with the section 4 with the deep learning and CNN method. The Result is then compared with the existing system that is accessed with the classification of image with the fashion is explained with section 5 and finally the result is concluded with section 6.

Methodology

Neural Networks Classification

The classification is classified with the different input image that is categorized with seven steps. This keras wrapper is sued with the Fujisan model that is proposed with the neural network that is used with the layer that connects the Softmax that is accessed with the functionalities that transform the linear transformation with calculate the input, output and other weight and height with the output size. This layer will compute the scores that ensure the classification criteria with the convolutional neural network that is connected and analyzed with the activation of the transformation with the system. This is described with the convolution method which describes the neuron. The neuron is provided with the activation based on the size of the output. The content based filtering technique is provided with different approach that uses the historical data with collaborative filtering method. The core automation process is classified with the process increases the classification using the machine learning process based on classification that uses segmentation with deep learning techniques. This convolutional neural network is used with the image transformation that is used with the consumer for classifying the image based category. This connects the input with the similar features that is measured with CNN that is accomplished with the classification. This is provided with the various recommendations that is classified with the image recognition that uses the cluster based on the categorization. This is widely used with the recommendation of the convolutional neural network that is used with the image processing or recognizing the data with the categorization of image with the proposed strategy.

The convolutional neural network is used with the success approach that uses the deep leaning techniques that provides the right output with detection of multiple objects that is classified with the given image that is used with multi scale windows that are used with the sliding window and deep learning approach used with the object detection in fashion. This is provided with the different algorithm that is entirely depends in the team with the use of CNN approach that proposes the region based on many problems. The first proposal is provided with the method that is developed with the RCNN enhanced with the feature extraction that is used with the training, evaluation and testing.

The scarcity of the labeled dataset is detailed with the deep learning techniques with many disadvantages as the prevention of over fitting. The public dataset is supervised with the object detection and recognition techniques that are identified with the colored image which is accepted with the resolution based deep learning techniques.

The neural network has many disadvantages and the deep learning techniques with more amounts of data for two reasons. This neural network has different parameter like samples with the deep learning techniques that is provided with the resolution techniques that us suffered with the deep resolution. The public dataset is acceptable based on the deep learning with resolution with the fashion industry based on the detailed fashion dataset with similar image based on 28×28 images with 10 classes. This approach is used with two different types of methods that is used for performing the match with masked template that provides the specific cases with more accurate result that is used with the convolutional neural network. This is used with the CNN approach that is fine tuned with the CNN trained data that is processed with the accurate result that is specialized with the neural model which pertains the personalized model with different layers. The trained dataset is personalized with the number if layers with the network with the use of neural network with the processing dataset.

Machine Learning

Machine learning approach is the emergent method that develops the software with the computer vision with the token recognition. The machine learning is used with the different strategic data acquisition based on the artificial intelligence which enables the business model. The convolutional neural network is the state of art that is used with the classification techniques. The dataset is considered for developing the various standards that is used with the dataset which provides the benchmarking criteria with the performance with dataset that is used with the next generation techniques. The neural network is created with the multiple models that inspire the bit prediction based techniques which is developed with the computational power with the connection established with neurons. The similarity algorithm was used with the traditional method that is provided with the robustness and performance.

Simulations

Fashion Dataset

Fashion dataset is taken from Kaggle repository which contains 70000 images. The fashion dataset is used with the distribution of the data which is used with the architectural features that is used with convolutional neural network that is modeled with epoch 1 with the flow based model. This architecture is proposed with the new model that is trained with the epoch’s which may be used for increasing the consistency, performance and accuracy. The large dataset is provided with the purpose that compete the relative features with the multiple finding that is classified with the image that is based suite with the image classification which retrieves the best matching classification. The object detection and the computer vision is provided with the improvement based generic system used with the real world application based on the successful approaches. This is provided with the enhanced approach that is proposed with the previous method that uses the object detection.

	Training	Validation	Testing
6912	4838	691	1382
10141	7099	1014	2028
6599	4619	660	1320
5560	3892	556	1112
6809	4766	681	1362
7103	4972	710	1421
6640	4648	664	1328
6010	4207	601	1202
6792	4755	679	1358
7434	5204	743	1487

70000	49000	7000	14000
			70000

Table No: 1 Dataset description

The generalization which is improved with the fashion detection for extracting the image with the speeded up robust features. The fast detection method is presented with the image recognition and personal detection techniques that are detected based on integral image which is used with the object detection techniques. The object detection is used with the deep learning algorithm that uses the various convolutional neural networks that is used with the ConvNets. This is generalizes the object with the rotation of the feature that is used with the processing the decentralization of the detection and descriptors.

Encoding and Decoding Representation

The encoder and decoder representation is provided with the general approach that is used with the neural machine translation that is used with encoder and decoder. The different encoder is used with the bag of world encoder with the convolutional encoder with the attention based encoder. This is provided with the input sentence that is used with the neural machine translation that may be encoded or decoded with the information that is used with the encoding the data with the discarding process based on the object detection.

This is processed with the input and output which is used with the time delay based on the procedures and policies based on the simple attention which may be provide the bag or words with the model which is used with time delay convolutional layer.

This model is evaluated with the determination and generation of the approach that is based on the machine learning approach. The team breach is evaluated with the generation based on machine learning approaches. This model is evaluated based on the different standard that is processed with the generation that uses the 500 images that us summarized with the web services which pairs the human that generates the various sentences with the various dataset that is sued with the different problem which is arise with the dataset for fashion.

The dataset consist of various images for object detection with the various state of art that measures the object or image recognition based on the state of art result with the fashion dataset that is used with the image features with the object detection methods. This is employed with the classification and uses the pure deep learning algorithm that is proposed with the improvement called R-CNN with speed based on the training process that enhances the approach for further improvement.

This development is similar with the faster R-CNN with the region proposal network that is used with the same architecture that is used with the CNN for object modeling and performs the various performances that is provided with the different development hat is analyzed with the similar extraction in VGG network. This is achieved with the single shot multibox detector method that supports the second enhancement with the version that is achieved with the result which is provided with the deep learning techniques. The samples were collected and matched with the sample large dataset that is provided with the different dataset that is labeled with the deep learning techniques.

Result Obtained

The experiment is conducted with the first evaluation techniques that may be used with the various architecture which is trained and then the follows the various dense architecture which is provided with the classification of message which is experimented with 2616 images that may be used with the classification model for designing and classifying the image with the convolutional layers that is used with the training the epoch based on the over fitting prevention. The accuracy of the result is provided with the dense layer and training epoch is considered with the various numbers of images with the validation process.

This dataset is divided with the trained dataset that may have the various images that is validated with the trained dataset based on the different process and toes of classification based in fashion. This dataset is used with the experiment for composing the 2616 images that is sued with the logo detection and the accuracy. This result is produced with the machine learning algorithm to prevent the number of images with the dataset for identification of logo in the dataset. Accuracy is always measured with the various validation set that is used with the different images based on the training data set. The feedback is analyzed with the various training that uses the epoch with same and different numbers with classification. The result is identified with vision model for analyzing the accuracy with the object and image detection for the given fashion dataset.

The experiment is conducted simple to predict the accuracy that is established with the deep learning method for scratching for recognition. The main reason with the major enhancement is trained based on the subsequent approach that is recognized based on the machine learning and deep learning with the system. This is provided with the various techniques that is provides the VGG 16 and VGG 19 for increasing the number of layers with the frozen network.

This is classified based on the computation process that is used with the similar execution time. The deep learning method is the algorithm that specifies the domain with the under investigated fashion with the refined method that is used with the business process. The classification is classified with the different business process that is used with the computer vision based on the state of art with the challenging application that describes the system and discuss the various challenges with the system development with different purpose.

Critical Analysis of Results

The input is provided with the 2500 nodes based on the 150×150 pixels that is sued with the zero padding is conducted with the various sides of the convolutional layer that is used with the forwarding the process of learnable filter that is used with the prediction of height and width of the image based on the filtering techniques used with the system. This process is mapped with the framework that proposes the convolutional neural network that is accomplished with the CNN architecture for increasing the efficiency and provides the accurate result with the classification. This forward the input to the convolutional layer that is sued with the height and width of the image based on 64 filters that is sued with the maxpool layer that is analyzed with the different layers that is analyzed based on the process that is activated and identify the coefficient values with the bias accumulation when the object is detected. The output is provided with the convolutional layer that is forwarded with the maxpool layer to the other proposed framework that maps the down sample mechanism which is used with the activation of project with fashion. The second method id accomplished based on the convolutional layer prediction for CNN architecture.

The most common approach used with the CNN architecture is RELU layer that applies the non linear activation of the data that is trained with the different images. This dataset is tested with the second convolutional layer that may yield 128 feature map which is trained based on VGG16 is provided with the ADAM optimizer based on class entropy is provided at the learning a rate if the system. This is provided with the use of dropout facility that is used with the cross entropy techniques for measuring the learning rate with the Softmax. This model is accessed with the proposed system and considers the dataset for classification, evaluation and other accuracy method with the system. The new dataset is analyzed with the other image and then train the data and produce the result based on the compared dataset. This is analyzed with the evaluation techniques that are used with the RMSE accuracy measure for performing the result based on the fashion dataset.

Conclusion

Visual classification is the vast and important features considered with the object detection and extracting the features in computer vision. This image is classified with the different sources that are accessed with the various steps in visual analysis for analyzing, extracting and recognizing various features based on the final extraction of the data with the 3D image machine learning or deep learning approach. Machine learning approach is the emergent method that develops the software with the computer vision with the token recognition. The fashion dataset is used with the distribution of the data which is used with the architectural features that is used with convolutional neural network that is modeled with epoch 1 with the flow based model. This is achieved with the single shot multibox detector method that supports the second enhancement with the version that is achieved with the result which is provided with the deep learning techniques. The experiment is conducted simple to predict the accuracy that is established with the deep learning method for scratching for recognition. This dataset is tested with the second convolutional layer that may yield 128 feature map which is trained based on VGG16 is provided with the ADAM optimizer based on class entropy is provided at the learning a rate if the system. The overall accuracy of training phase is 93.18%, Validation phase is 92.18% and testing phase is 90.69%.

References

Alhanjour, M.A. (2018). Improved HMM by Deep Learning for Ear Classification. International Journal of Innovative Research in Computer Science & Technology, 6(3), pp.36–42.
Cao, Y., Shen, C. and Shen, H.T. (2017). Exploiting Depth From Single Monocular Images for Object Detection and Semantic Segmentation. IEEE Transactions on Image Processing, 26(2), pp.836–846.
Donati, L., Iotti, E., Mordonini, G. and Prati, A. (2019). Fashion Product Classification through Deep Learning and Computer Vision. Applied Sciences, 9(7), p.1385.
Esposito, A. and Malerba, D. (2017). Guest Editorial: Deep Learning in Computer Vision. IET Computer Vision, 11(8), pp.621–622.
Guan, L., Wu, Y. and Zhao, J. (2018). SCAN: Semantic Context Aware Network for Accurate Small Object Detection. International Journal of Computational Intelligence Systems, 11(1), p.936.
Keerthi Gorripati, S. and Angadi, A. (2018). Visual Based Fashion Clothes Recommendation with Convolutional Neural Networks. [online] papers.ssrn.com. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3363823 [Accessed 16 May 2020].
Park, H.S. (2000). Morphological image segmentation for realistic image representation preserving semantic object shapes. Optical Engineering, 39(7), p.1909.
Pujadas, E.R. and Reisert, M. (2014). Shape-Based Normalized Cuts Using Spectral Relaxation for Biomedical Segmentation. IEEE Transactions on Image Processing, 23(1), pp.163–170.
Putra, G. (2019). Classification of C2C e-Commerce Product Images using Deep Learning Algorithm. International Journal of Advanced Computer Science and Applications, 10(9).
Shetty, S.K. and Siddiqa, A. (2019). Deep Learning Algorithms and Applications in Computer Vision. International Journal of Computer Sciences and Engineering, 7(7), pp.195–201.
Singh, I. and Kumar, D. (2011). A Review on Different Image Segmentation Techniques. Indian Journal of Applied Research, 4(4), pp.1–3.
Karthika, R.K. and lakshmi, S.. S. (2017). Object Detection and Semantic Segmentation using Neural Networks. International Journal of Computer Trends and Technology, 47(2), pp.95–100.
Upadhyay, N. (2019). A Review- Image Segmentation using Soft Computing Techniques. International Journal for Research in Applied Science and Engineering Technology, 7(5), pp.904–910.
Waseem Khan, M. (2014). A Survey: Image Segmentation Techniques. International Journal of Future Computer and Communication, [online] pp.89–93. Available at: http://ijfcc.org/papers/274-B317.pdf [Accessed 9 Dec. 2018].
Wu, M., Zhang, C., Liu, J., Zhou, L. and Li, X. (2019). Towards Accurate High Resolution Satellite Image Semantic Segmentation. IEEE Access, pp.1–1.