Combining visual features of an image at different precision value of unsupervised content based image retrieval

Zakariya, Syed Mohammad; Ali, Rashid; Ahmad, Nesar

doi:10.1109/iccic.2010.5705739

Cited by 31 publications

(10 citation statements)

References 13 publications

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“…The cluster methods have been applied in many image retrieval problems such as apply K‐mean algorithm and Euclidean distance to cluster and apply in image retrieval (Lin, Chen, Lee, & Liao, ), build CBIR using K‐mean algorithm and Mahalanobis distance between colour feature vectors of images (Banerjee, Bandyopadhyay, & Pal, ), use the K‐mean algorithm and MPEG7 palette to cluster and retrieve similarity images (Saboorian, Jamzad, & Rabiee, ), combine the scale feature of colours, texture, shape to cluster, and query similar images (Zakariya, Ali, & Ahmad, ), cluster images based on colours and K‐mean algorithm (An, Baek, Shin, Chang, & Park, ), query image by content based on clusters of images and the unsupervised learning (Chen, Wang, & Krovetz, ), and so forth.…”

Section: Image Retrieval Using Cluster Graphmentioning

confidence: 99%

Content‐based image retrieval based on binary signatures cluster graph

2017

Expert Systems

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In this paper, we approach a method of clustering binary signature of image in order to create a clustering graph structure for building the content‐based image retrieval. First, the paper presents the segmentation method based on low‐level visual features including colour and texture of image. On the basis of segmented image, the paper creates binary signature to describe location, colour, and shape of interest objects. In order to match similar images, the paper presents a similarity measure between the images based on binary signature. From that, the paper proposes the method of clustering binary signature to quickly query similar images. At the same time, the graph data structure is built using the partition cluster technique and the rules of binary signatures' distribution of images. On the basis of data structure, we propose a graph creation algorithm, a cluster splitting/merging algorithm, and a similarity image retrieval algorithm. To illustrate the proposed theory, we build an image retrieval application and assess the experimental results on the image datasets including COREL (1,000 images), CBIR images (1,344 images), WANG (10,800 images), MSRDI (15,720 images), and ImageCLEF (20,000 images).

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Section: Image Retrieval Using Cluster Graphmentioning

confidence: 99%

Content‐based image retrieval based on binary signatures cluster graph

2017

Expert Systems

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“…The proposed method is simple yet effective and improves over the state-of-the-art large-scale face image retrieval system. Zakariya S M et al [15] combined some percentage value of two features namely color-texture features and color-shape features and also take the union of these two features. Their experiments showed the combination of features to be robust for image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Multiview Face Retrieval in Surveillance Video by Active Training Sample Collection

Pei

2014

2014 Tenth International Conference on Computational Intelligence and Security

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For multiview face retrieval of certain person in surveillance video, a key challenge is the lack of training samples. Generally, the law enforcement agencies usually have only one frontal view face image of the target person, however, the faces of the target person in the surveillance video could be in different orientation, and it is impossible for a classifier trained on only frontal view face to retrieve the faces under other orientation. This paper proposes an active training sample collection method for multiview face retrieval in surveillance video. First, the front view face image is used to train a classifier to retrieve the target person's front view face in videos. As the video is continuous, we can track the face and obtain side view faces of the target person. Then these selected side view faces are combined with the frontal view face to form a new training data set. The classifier is updated based on the new training data set, and can retrieve multiview faces of the target people. The experimental results prove the effectiveness of the proposed method.

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“…Huge amount of image data is generated everyday due to rapid evolution of image capturing devices. [1] Classification of images inside the database stimulates the searching process efficiency. Image classification creates an organized database by cataloging the images into analogous categories and objects.…”

Section: Introductionmentioning

confidence: 99%

Performance Boost of Block Truncation Coding based Image Classification using Bit Plane Slicing

Kekre¹,

Thepade²,

Das³

et al. 2012

IJCA

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Image classification demands major attention with increasing volume of available image data. The paper has shown performance boosting of image classification after associating Bit Plane Slicing with Block Truncation Coding (BTC) for feature extraction. Here more significant bit planes were considered for extraction of feature vectors. RGB color space was considered to carry out the experimentation. A database of 900 images was used for evaluation purpose.

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Combining visual features of an image at different precision value of unsupervised content based image retrieval

Cited by 31 publications

References 13 publications

Content‐based image retrieval based on binary signatures cluster graph

Content‐based image retrieval based on binary signatures cluster graph

Multiview Face Retrieval in Surveillance Video by Active Training Sample Collection

Performance Boost of Block Truncation Coding based Image Classification using Bit Plane Slicing

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