ABSTRAT:

The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.

EXISTING SYSTEM :

From a decade, a shift has been observed in feature representation from hand-engineering to learning-based after the emergence of deep learning [32], [33]. This transition is depicted in Fig. 1 where the convolutional neural networks based feature learning replaces the state-of-the-art pipeline of traditional hand-engineered feature representation. The deep learning is a hierarchical feature representation technique to learn the abstract features from data which are important for that dataset and application [34]. Based on the type of data to be processed, different architectures came into existence such as Artificial Neural Network (ANN)/ Multilayer Perceptron (MLP) for 1-D data [35], [36], Convolutional Neural Networks (CNN) for image data [37], [38], and Recurrent Neural Net- works (RNN) for time-series data [39], [40]. A huge progress has been made in this decade to utilize the power of deep learning for content based image retrieval [32], [41], [42], [43], [44]. Thus, this survey mainly focuses over the progress in state-of-the-art deep learning based models and features for content based image retrieval from its inception. A taxonomy for the same is portrayed in Fig. 2. The major contributions of this survey can be outlined as follows:

EXISTING SYSTEM DISADVANTAGES:

1.LESS ACCURACY

2. LOW EFFICIENCY

PROPOSED SYSTEM :

the initial attempts, in 2011, Kievsky and Hinton have used a deep autoencoder to map the images to short binary codes for content based image retrieval (CBIR) [58]. Kang et al. (2012) have proposed a deep multi-view hashing to generate the code for CBIR from multiple views of data by modelling the layers with view- specific and shared hidden nodes [59]. In 2013, Wu et al. have considered the multiple pretrained stacked denoising autoencoders over low features of the images [60]. They also fine tune the multiple deep networks on the output of the pretrained autoencoders. 2) 2014: In an outstanding work, the activations of the top layers of a large convolutional neural network (CNN) are utilized as the descriptors (neural codes) for image retrieval [61] as depicted in Fig. 4. A very promising performance has been recorded using the neural codes for image retrieval even if the model is trained on un-related data. The neural code is compressed using principal component analysis (PCA) to generate the compact descriptor. In 2014, deep ranking model is investigated by learning the similarity metric directly from images

PROPOSED SYSTEM ADVANTAGES:

1.HIGH ACCURACY

2.HIGH EFFICIENCY

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10