Separable vocabulary and feature fusion for image retrieval based on sparse representation

Wang, Yanhong; Cen, Yigang; Zhao, Ruizhen; Cen, Yi; Hu, Shaohai; Voronin, Viacheslav; Wang, Hengyou

doi:10.1016/j.neucom.2016.08.106

Cited by 16 publications

(7 citation statements)

References 21 publications

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“…The experimental analysis on various sizes of the visual vocabulary using a proposed method that employs visual words integration is performed with its competitor CBIR methods and standard CBIR methods [59,60], whose experimental details are shown in Figure 12 and Table 4, respectively. The comparative analysis presented in Figure 12 and Table 4 clearly indicates the robustness of the proposed method that employs visual words integration as compared with its competitor CBIR methods and standard CBIR methods [59,60]. The best performance of the proposed method and its competitor methods are highlighted in Figure 12.…”

Section: Experimental Parameters Results and Discussionmentioning

confidence: 99%

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

Sarwar

Mehmood

Saba

et al. 2018

Journal of Information Science

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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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Section: Experimental Parameters Results and Discussionmentioning

confidence: 99%

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

Sarwar

Mehmood

Saba

et al. 2018

Journal of Information Science

View full text Add to dashboard Cite

show abstract

“…Therefore, in (19) we distill the informative clues from pixels' intensities in resultant intensity map while suppressing the least useful ones by quantizing into N I intervals. The quantized intensity map for f (x, y) are represented by (20).…”

Section: Intensity Map Extractionmentioning

confidence: 99%

“…Image retrieval technique in [19], [70] utilized the Bag of Visual Words (BoVW) by incorporating textural and color characteristics of an image for retrieving relevant images. The method in [20] used sparse representation of data with BoVW to reduce curse of dimensionality. BoVW-based methods perform better but, they are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Correlated Microstructure Descriptor for Image Retrieval

et al. 2019

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In this paper, a novel feature descriptor named ''correlated microstructure descriptor (CMSD)'' is proposed for image retrieval. CMSD represents high level semantics by identifying microstructures via establishing correlations between texture orientation, color, and intensity features. The proposed CMSD follows a two-staged approach for feature extraction and their integration. Color information is extracted by exclusively quantizing each component of HSV color space. Richer edge orientation information is extracted by using the multi-directional Sobel operator. Local contrast information is obtained by quantizing V component of HSV. Correlated microstructures are then identified by correlations established on the basis of proximity and continuation relations. The identified microstructures are then mapped to color, texture orientation, and intensity features exclusively to obtain micro-color, micro-orientation, and micro-intensity maps, respectively. Proposed three concatenated micro-maps are represented through a 2-D histogram, incorporate information regarding semantic and spatial contents of an image. The experiments are performed on standard datasets, i.e., Corel 1k, Corel 5k, and Corel 10k. The results evaluation depicts the outstanding performance of proposed CMSD as compared to state-of-the-art methods including TCM, GLCM, MTH, MSD, MTSD, and SED. INDEX TERMSContent based image retrieval, correlated low-level visual features, correlated primary visual features, micro-maps, microstructures.

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“…Some recent color descriptors are local color occurrence descriptor [45], rotation invariant structure based descriptor [46], illumination invariant color descriptor [47], etc. Other notable image retrieval methods are based on SURF binarization and fast codebook construction [48], feature fusion using compressed sensing [49], and feature fusion using separable vocabulary [50].…”

Section: Arxiv:170906508v2 [Cscv] 4 Dec 2018mentioning

confidence: 99%

Face retrieval using frequency decoded local descriptor

Dubey

2018

Multimed Tools Appl

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The local descriptors have been the backbone of most of the computer vision problems. Most of the existing local descriptors are generated over the raw input images. In order to increase the discriminative power of the local descriptors, some researchers converted the raw image into multiple images with the help of some high and low pass frequency filters, then the local descriptors are computed over each filtered image and finally concatenated into a single descriptor. By doing so, these approaches do not utilize the inter frequency relationship which causes the less improvement in the discriminative power of the descriptor that could be achieved. In this paper, this problem is solved by utilizing the decoder concept of multi-channel decoded local binary pattern over the multi-frequency patterns. A frequency decoded local binary pattern (FDLBP) is proposed with two decoders. Each decoder works with one low frequency pattern and two high frequency patterns. Finally, the descriptors from both decoders are concatenated to form the single descriptor. The face retrieval experiments are conducted over four benchmarks and challenging databases such as PaSC, LFW, PubFig, and ESSEX. The experimental results confirm the superiority of the FDLBP descriptor as compared to the state-of-the-art descriptors such as LBP, SOBEL LBP, BoF LBP, SVD S LBP, mdLBP, etc.

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Separable vocabulary and feature fusion for image retrieval based on sparse representation

Cited by 16 publications

References 21 publications

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

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

Correlated Microstructure Descriptor for Image Retrieval

Face retrieval using frequency decoded local descriptor

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