Development of an Image Retrieval Model for Biomedical Image Databases

2011 ◽

pp. 52-80

Author(s):

Khalifa Djemal ◽

Hichem Maaref

Keyword(s):

Image Retrieval ◽

Image Recognition ◽

Clinical Medicine ◽

Recognition Performance ◽

Recognition System ◽

Image Databases ◽

Biomedical Image ◽

Intelligent Information ◽

Research And Education ◽

Information Description

There is a significant increase in the use of biomedical images in clinical medicine, disease research, and education. While the literature lists several successful methods that were developed and implemented for content-based image retrieval and recognition, they have been unable to make significant inroads in biomedical image recognition domain. The use of computer-aided diagnosis has been increasing. It is based on descriptors extraction and classification approaches. This interest is due to the need for specialized methods, which are specific to each biomedical image type, and also due to the lack of advances in image recognition systems. In this chapter, the authors present intelligent information description techniques and the most used classification methods in an image retrieval and recognition system. A multicriteria classification method applied for sickle cells disease image databases is given. The recognition performance system is illustrated and discussed.

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Evaluation of Light Inspired Optimization Algorithm-Based Image Retrieval

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.573.529 ◽

2014 ◽

Vol 573 ◽

pp. 529-536

Author(s):

T. Kanimozhi ◽

K. Latha

Keyword(s):

Image Retrieval ◽

Firefly Algorithm ◽

Retrieval System ◽

Computation Time ◽

Image Databases ◽

Image Search ◽

Retrieval Process ◽

Large Collection ◽

Image Retrieval System ◽

Unique Method

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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A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

Journal of Information Science ◽

10.1177/0165551518782825 ◽

2018 ◽

Vol 45 (1) ◽

pp. 117-135 ◽

Cited By ~ 15

Author(s):

Amna Sarwar ◽

Zahid Mehmood ◽

Tanzila Saba ◽

Khurram Ashfaq Qazi ◽

Ahmed Adnan ◽

...

Keyword(s):

Image Retrieval ◽

Image Databases ◽

Semantic Gap ◽

Content Based Image Retrieval ◽

Support Vector ◽

Bag Of Words ◽

Visual Words ◽

Visual Attributes ◽

Visual Vocabulary ◽

Novel Method

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

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