Image retrieval based on effective feature extraction and diffusion process

Zhou, Juxiang; Liu, Xiaodong; Liu, Wanquan; Gan, Jianhou

doi:10.1007/s11042-018-6192-1

Cited by 30 publications

(19 citation statements)

References 48 publications

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“…It can be seen that the proposed work performs better than the identified existing approaches. The [74] Gabor Histogram 41.30 Yu et al [75] Image-based HOG-LBP 46.00 Deselaers et al [74] LF-SIFT Histogram 48.20 Hamreras and Bucheham [76] RICE Algorithm selection model 49.72 Deselaers et al [74] Color histogram 50.50 Srivastava et al [77] Moments of LTP 53.70 Kumar and Nagarajan [51] LCP 78.30 Mohiuddin et al [68] CCV + LBP 82.52 Srivastava and Khare [78] DWT + SURF + GLCM 84.97 Pardede et al [79] Low-level features + relevance feedback 85.59 Srivastava and Khare [49] MS-LBP (7 Thus, the proposed technique surpasses over the literature of Xia et al [48], Srivastava et al [73,77,80], Vipparthi and Nagar [52], Srivastava and Khare [49], Zhou et al [69], Verma et al [81], Zhou et al [71], and Mohiuddin et al [68], as shown in Table 4.…”

Section: Performance On Corel-5kmentioning

confidence: 97%

“…The ARP performance story over Corel-10k dataset can be summarized as follow: it overshadows color moments [73] by 41.34%, moments of wavelet transform [80] by [67] DCD + wavelet + curvelet 76.50 Mohiuddin et al [68] CCV + LBP 76.60 Zhou et al [69] CH + LDP 78.31 Zhou et al [70] SGLCM + 2D-CS model 80.10 Zhou et al [71] CH + LDP +SIFTBOF 85.10 Pradhan et al [72] Adaptive…”

Section: Performance On Corel-10kmentioning

confidence: 99%

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Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

Chavda

Goyani

2020

SN COMPUT. SCI.

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The objective of the content-based image retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependence of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors: global color histogram and multi-scale local binary pattern (MS-LBP); furthermore, the use of PCA for dimension reduction and LDA for the selection of features. The proposed method is evaluated concerning various benchmark datasets, viz., Corel-1k, Corel-5k, Corel-10k, and Ghim-10k together with result comparison based on the precision and recall values at different thresholds. The classification purposes are satisfied with Euclidean and City Block distance. The performance study of the proposed work displays it as outperformer than the identified state-of-the-art literature.

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Section: Performance On Corel-5kmentioning

confidence: 97%

Section: Performance On Corel-10kmentioning

confidence: 99%

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

Chavda

Goyani

2020

SN COMPUT. SCI.

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“…Feature extraction is the process in which the features are extracted from the input data and transferred into a set of features, splitting the accurate data from the massive set of databases and retrieving the appropriate and revealing features (also known as dimensionality reduction). In this stage, the image features (texture and color) are extracted [23].…”

Section: Feature Extractionmentioning

confidence: 99%

An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier

Ghrabat

Maolood

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Image retrieval is the process of retrieving images from a database. Certain algorithms have been used for traditional image retrieval. However, such retrieval involves certain limitations, such as manual image annotation, ineffective feature extraction, inability capability to handle complex queries, increased time required, and production of less accurate results. To overcome these issues, an effective image retrieval method is proposed in this study. This work intends to effectively retrieve images using a best feature extraction process. In the preprocessing of this study, a Gaussian filtering technique is used to remove the unwanted data present in the dataset. After preprocessing, feature extraction is applied to extract features, such as texture and color. Here, the texture feature is categorized as a gray level cooccurrence matrix, whereas the novel statistical and color features are considered image intensity-based color features. These features are clustered by k-means clustering for label formation. A modified genetic algorithm is used to optimize the features, and these features are classified using a novel SVMbased convolutional neural network (NSVMBCNN). Then, the performance is evaluated in terms of sensitivity, specificity, precision, recall, retrieval and recognition rate. The proposed feature extraction and modified genetic algorithm-based optimization technique outperforms existing techniques in experiments, with four different datasets used to test the proposed model. The performance of the proposed method is also better than those of the existing (RVM) regression vector machine, DSCOP, as well as the local directional order pattern (LDOP) and color co-occurrence feature + bit pattern feature (CCF + BPF) methods, in terms of the precision, recall, accuracy, sensitivity and specificity of the NSVMBCNN.

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“…If the retrieval system has better performance, the curve is as far from the origin of coordinates as possible. The area between the curve and the X-Y axes should be larger, which is usually measured and is approximate to MAP [42]. In other words, the most common way to summarize the P-R curve in one value is P-R. P-R is the mean of the average precision (AP) scores of all queries and is computed as follows:…”

Section: Mean Average Precision (Map)mentioning

confidence: 99%

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

et al. 2018

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The requirement for effective image search, which motivates the use of Content-Based Image Retrieval (CBIR) and the search of similar multimedia contents on the basis of user query, remains an open research problem for computer vision applications. The application domains for Bag of Visual Words (BoVW) based image representations are object recognition, image classification and content-based image analysis. Interest point detectors are quantized in the feature space and the final histogram or image signature do not retain any detail about co-occurrences of features in the 2D image space. This spatial information is crucial, as it adversely affects the performance of an image classification-based model. The most notable contribution in this context is Spatial Pyramid Matching (SPM), which captures the absolute spatial distribution of visual words. However, SPM is sensitive to image transformations such as rotation, flipping and translation. When images are not well-aligned, SPM may lose its discriminative power. This paper introduces a novel approach to encoding the relative spatial information for histogram-based representation of the BoVW model. This is established by computing the global geometric relationship between pairs of identical visual words with respect to the centroid of an image. The proposed research is evaluated by using five different datasets. Comprehensive experiments demonstrate the robustness of the proposed image representation as compared to the state-of-the-art methods in terms of precision and recall values.

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Image retrieval based on effective feature extraction and diffusion process

Cited by 30 publications

References 48 publications

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

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