Content Based Image Retrieval (CBIR) by Statistical Methods

An image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T- test to measure the independence between more than images, (coefficient of correlate, T- test, Level of significance, find the decision), and, through experimental test, it was found that this proposed method of retrieval technique is powerful than the classical retrieval System.

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Visual interfaces for a semantic content-based image retrieval system

10.1117/12.476306 ◽

2003 ◽

Author(s):

Hagit Z. Hel-Or ◽

Dov Dori

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Semantic Content ◽

Content Based Image Retrieval ◽

Image Retrieval System ◽

Visual Interfaces

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Content-Based Image Retrieval System for Real Images

10.29007/w4sr ◽

2018 ◽

Author(s):

Yin-Fu Huang ◽

Bo-Rong Chen

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Image Database ◽

Multimedia Data ◽

Content Based Image Retrieval ◽

Real Image ◽

Query Image ◽

Image Retrieval System ◽

Similar Images ◽

Network Technologies

With the rapid progress of network technologies and multimedia data, information retrieval techniques gradually become content-based, and not text-based yet. In this paper, we propose a content-based image retrieval system to query similar images in a real image database. First, we employ segmentation and main object detection to separate the main object from an image. Then, we extract MPEG-7 features from the object and select relevant features using the SAHS algorithm. Next, two approaches “one-against- all” and “one-against-one” are proposed to build the classifiers based on SVM. To further reduce indexing complexity, K-means clustering is used to generate MPEG-7 signatures. Thus, we combine the classes predicted by the classifiers and the results based on the MPEG-7 signatures, and find out the similar images to a query image. Finally, the experimental results show that our method is feasible in image searching from the real image database and more effective than the other methods.

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Unsupervised Content Based Image Retrieval by Combining Visual Features of an Image With A Threshold

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2012.1131 ◽

2012 ◽

pp. 149-154

Author(s):

S. M. Zakariya ◽

Rashid Ali ◽

Nesar Ahmad

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Image Database ◽

The Other ◽

Content Based Image Retrieval ◽

Visual Features ◽

Query Image ◽

System A ◽

Image Retrieval System

Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape and texture to represent and index the image. In a typical content based image retrieval system, a set of images that exhibit visual features similar to that of the query image are returned in response to a query. CLUE (CLUster based image rEtrieval) is a popular CBIR technique that retrieves images by clustering. In this paper, we propose a CBIR system that also retrieves images by clustering just like CLUE. But, the proposed system combines all the features (shape, color, and texture) with a threshold for the purpose. The combination of all the features provides a robust feature set for image retrieval. We evaluated the performance of the proposed system using images of varying size and resolution from image database and compared its performance with that of the other two existing CBIR systems namely UFM and CLUE. We have used four different resolutions of image. Experimentally, we find that the proposed system outperforms the other two existing systems in ecery resolution of image.

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Image Retrieval System using Residual Neural Network in a Distributed Environment

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9811.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 4597-4605

Keyword(s):

Neural Network ◽

Image Retrieval ◽

Retrieval System ◽

Image Data ◽

Content Based Image Retrieval ◽

Features Selection ◽

Query Image ◽

Server Side ◽

Image Retrieval System ◽

Client Side

Development of Content-Based Image Retrieval systems supports retrieval of similar images based on selected features. Selection of appropriate features for this process is a difficult task. In this regard, deep learning concept helps in choosing appropriate features for retrieval. In this work, Content-Based Image Retrieval system is proposed using Convolution Neural Network known as Residual Neural Network model. The dataset used to build retrieval system is collection of web images 50,000 of 250 categories. The model is trained on 40% of image data and tested on 60% of data. When user submits a query image from the client-side, similar features are extracted by the model on server-side. Later, the features of query image are compared with trained images data and similarity is measured using the metric of Euclidean distance. The retrieved resultant images are displayed on Graphical User Interface. The results are comparatively higher with the existing systems. The proposed work is also compared with Google’s Image retrieval system for random query images and our proposed work has shown a better performance by 14.27%.

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