Evaluation of Light Inspired Optimization Algorithm-Based Image Retrieval

Image retrieval system becoming a more popular in all the disciplines of image search. In real-time, interactive image retrieval system has become more accurate, fast and scalable to large collection of image databases. This paper presents a unique method for an image retrieval system based on firefly algorithm, which improve the accuracy and computation time of the image retrieval system. The firefly algorithm is utilized to optimize the image retrieval process via search for nearly optimal combinations between the corresponding features as well as finding out approximate optimized weights for similarities with respect to the features. The proposed method is able to dynamically reflect the user’s intention in the retrieval process by optimizing the objective function. The Efficiency of the proposed method is compared with other existing image retrieval methods through precision and recall. The performance of the method is experimented on the Corel and Caltech database images.

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Enhancement of Resulting Image Search Engine (ERISE) by Content-Based Image Retrieval System

2020 IEEE Region 10 Symposium (TENSYMP) ◽

10.1109/tensymp50017.2020.9230653 ◽

2020 ◽

Author(s):

Sumaiya ◽

Md. Armanuzzaman ◽

Md Armanuzzaman

Keyword(s):

Image Retrieval ◽

Search Engine ◽

Retrieval System ◽

Content Based Image Retrieval ◽

Image Search ◽

Image Retrieval System

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Image Retrieval Using Data Mining Technique

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.8.26 ◽

2020 ◽

pp. 2115-2125

Author(s):

Sarmad T. Abdul-Samad ◽

Sawsan Kamal

Keyword(s):

Image Retrieval ◽

User Satisfaction ◽

Clustering Algorithm ◽

Retrieval System ◽

Spatial Information ◽

Search Space ◽

Extraction Process ◽

Retrieval Process ◽

Data Mining Technique ◽

Image Retrieval System

Even though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show that using the spatial features increases the system accuracy and that the clustering algorithm speeds up the image retrieval process. This shows that the proposed system works with texture and non-texture images.

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Towards Large Scale Image Retrieval System Using Parallel Frameworks

10.5772/intechopen.94910 ◽

2020 ◽

Author(s):

Saliha Mezzoudj

Keyword(s):

Big Data ◽

Image Retrieval ◽

Large Scale ◽

Retrieval System ◽

Image Databases ◽

Content Based Image Retrieval ◽

Content Search ◽

Parallel Frameworks ◽

Image Retrieval System ◽

Large Scale Image Retrieval

Recently, the increasing use of mobile devices, such as cameras and smartphones, has resulted in a dramatic increase in the amount of images collected every day. Therefore, retrieving and managing these large volumes of images has become a major challenge in the field of computer vision. One of the solutions for efficiently managing image databases is an Image Content Search (CBIR) system. For this, we introduce in this chapter some fundamental theories of content-based image retrieval for large scale databases using Parallel frameworks. Section 2 and Section 3 presents the basic methods of content-based image retrieval. Then, as the emphasis of this chapter, we introduce in Section 1.2 A content-based image retrieval system for large-scale images databases. After that, we briefly address Big Data, Big Data processing platforms for large scale image retrieval. In Sections 5, 6, 7, and 8. Finally, we draw a conclusion in Section 9.

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Interactive Visualization Tool for Analysis of Large Image Databases

Visual Analytics and Interactive Technologies ◽

10.4018/978-1-60960-102-7.ch016 ◽

2011 ◽

pp. 266-281

Author(s):

Anca Doloc-Mihu

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Effective Interaction ◽

Qualitative Evaluation ◽

Image Databases ◽

Multiple Views ◽

End User ◽

Retrieval Systems ◽

Image Retrieval System ◽

Large Image Databases

Navigation and interaction are essential features for an interface that is built as a help tool for analyzing large image databases. A tool for actively searching for information in large image databases is called an Image Retrieval System, or its more advanced version is called an Adaptive Image Retrieval System (AIRS). In an Adaptive Image Retrieval System (AIRS) the user-system interaction is built through an interface that allows the relevance feedback process to take place. In this chapter, the author identifies two types of users for an AIRS: a user who seeks images whom the author refers to as an end-user, and a user who designs and researches the collection and the retrieval systems whom the author refers to as a researcher-user. In this context, she describes a new interactive multiple views interface for an AIRS (Doloc-Mihu, 2007), in which each view illustrates the relationships between the images from the collection by using visual attributes (colors, shapes, proximities). With such views, the interface allows the user (both end-user and researcher-user) a more effective interaction with the system, which, further, helps during the analysis of the image collection. The author‘s qualitative evaluation of these multiple views in AIRS shows that each view has its own limitations and benefits. However, together, the views offer complementary information that helps the user in improving his or her search effectiveness.

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