Content-based image retrieval using joint correlograms

Williams, Adam; Yoon, Peter A.

doi:10.1007/s11042-006-0087-2

Cited by 36 publications

(17 citation statements)

References 6 publications

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“…Zeng [15] introduced local level structure descriptor which extracts color, texture and shape as a single unit for image retrieval. Williams and Yoon [16] described joint autocorrelogram (JAC) that extracts color, texture, gradient and rank and is used effectively for image searching. Color and edge directivity descriptor (CEDD) reported in Chatzichristofis et al [17] computes textures from the six-bin histogram of the fuzzy system and color from the 24-bin color histogram formed by the 24-bin fuzzy-linking system.…”

Section: Related Workmentioning

confidence: 99%

An efficient content based image retrieval using enhanced multi-trend structure descriptor

Sathiamoorthy

Mohananthini

2020

SN Appl. Sci.

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This paper contributes a new variant of multi-trend structure descriptor (MTSD) for efficient content based image retrieval. The proposed variant of MTSD encodes color/edge orientation/texture quantized values versus orientation of equal, small and large trends instead of color/edge orientation/texture quantized values versus equal, small and large trends. In addition, it also encodes color/edge orientation/texture quantized values versus average location of distribution of pixel values for equal, small and large trends at each orientation. To reduce the time cost of the proposed variant of MTSD with the preservation of its accuracy, the image is decomposed into fine level using discrete Haar wavelet transform and the fine level for the decomposition of an image is determined empirically. Comprehensive experiments are conducted using the benchmark Corel-1k, Corel-5k, Corel-10k, Caltech-101, LIDC-IDRI-CT, VIA/I-ELCAP-CT and OASIS-MRI image datasets and the results evident that the proposed variant of MTSD achieves the state-of-the-art performance for natural, textural and biomedical image retrieval. Precision and recall are the measures used to measure the accuracy. Euclidean similarity measure is used to calculate the similarity information between query and target images.

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Section: Related Workmentioning

confidence: 99%

An efficient content based image retrieval using enhanced multi-trend structure descriptor

Sathiamoorthy

Mohananthini

2020

SN Appl. Sci.

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“…• Visual Color Descriptors: Border/Interior Pixel Classification (BIC) [41]; Global Color Histogram (GCH) [43] (both already used in Section 7.3); and the Joint Correlogram (JAC) [50]. Descriptor (HTD) [51]; Quantized Compound Change Histogram (QCCH) [52]; and Local Activity Spectrum (LAS) [47] (the last also considered in Section 7.4).…”

Section: Multimodal Retrievalmentioning

confidence: 99%

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Pedronette

Calumby

Torres

2015

J Image Video Proc.

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The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semisupervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.

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“…-Visual Color Descriptors: we considered three color descriptors on experiments: Border/Interior Pixel Classification (BIC) [50], Global Color Histogram (GCH) [51] (both already used on Section 6.1.2), and the Joint Correlogram (JAC) [59]. -Visual Texture Descriptors: for texture we used the Homogeneous Texture Descriptor (HTD) [60], Quantized Compound Change Histogram (QCCH) [19], and Local Activity Spectrum (LAS) [52] (the last also considered in Section 6.1.3).…”

Section: Descriptorsmentioning

confidence: 99%

Combining re-ranking and rank aggregation methods for image retrieval

Pedronette

Torres

2015

Multimed Tools Appl

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This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems.

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Content-based image retrieval using joint correlograms

Cited by 36 publications

References 6 publications

An efficient content based image retrieval using enhanced multi-trend structure descriptor

An efficient content based image retrieval using enhanced multi-trend structure descriptor

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Combining re-ranking and rank aggregation methods for image retrieval

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