Development of keyword-based image retrieval system in automatically annotated image database

2004 ◽

Vol 02 (01) ◽

pp. 59-73 ◽

Cited By ~ 4

Author(s):

PAWAN JAIN ◽

S. N. MERCHANT

Keyword(s):

Data Structure ◽

Image Retrieval ◽

Retrieval System ◽

Performance Enhancement ◽

Image Database ◽

Retrieval Systems ◽

Candidate Image ◽

Image Retrieval System ◽

Resolution Level ◽

Structure Algorithm

Most of the content-based image retrieval systems require a distance computation of feature vectors for each candidate image in the image database. This exhaustive search is highly time-consuming and inefficient. This limits the usefulness of such system. Thus there is a growing need for a fast image retrieval system. Multiresolution data-structure algorithm provides a good solution to the above problem. In this paper we propose a wavelet-based multiresolution data-structure algorithm. Wavelet-based multiresolution data-structure further reduce the number of computation by around 50%. In the proposed approach we reuse the information obtained at lower resolution levels to calculate the distance at a higher resolution level. Apart from this, the proposed structure saves memory overheads by about 50% over multiresolution data-structure algorithm. The proposed algorithm can be easily combined with other algorithms for performance enhancement.4 In this paper we use the proposed technique to match luminance histogram for image retrieval. Fuzzy histograms enhances performance by considering the similarity between neighboring bins. We have extended the proposed approach to fuzzy histograms for better performance.

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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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Designing Image Retrieval System with the Concept of Visual Keys

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2006.p0136 ◽

2006 ◽

Vol 10 (2) ◽

pp. 136-144 ◽

Cited By ~ 4

Author(s):

Manabu Serata ◽

◽

Yutaka Hatakeyama ◽

Kaoru Hirota

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Image Database ◽

Text Retrieval ◽

Image Feature ◽

Feature Clustering ◽

Retrieval Time ◽

Retrieval Systems ◽

Retrieval Efficiency ◽

Image Retrieval System

A concept of visual keys is proposed to provide efficient and useful content-based image retrieval systems to users. Visual keys are defined as representative sub-images which are extracted from an image database by using image feature clustering. The proposed system is implemented and is tested on 1,000 images, which are included in the COREL database. Although the system makes use of only 80 sub-images from 8,962 ones extracted from the image database, the performance is kept with 90%. The retrieval time is within 4ms on the proposed system, which has retrieval efficiency like that of text retrieval by being applied text retrieval techniques, and thus the system is expected to provide the services on the WWW.

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