Image Retrieval by Integrating Global Correlation of Color and Intensity Histograms with Local Texture Features

Kanaparthi, Suresh Kumar; Raju, U. S. N.; Shanmukhi, P.; G, Aneesha; Mohammed, Sharfuddin Waseem

doi:10.1007/s11042-019-08029-7

Cited by 22 publications

(13 citation statements)

References 43 publications

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“…Now, for each block, calculate the 8-bit code using the values in the DCT as given in Eqs. (12) to (19) and making into binary patterns as explained in Algorithm-2. The energy values used for calculating this are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

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Content-Based Image Retrieval Using Energy-Based Frequency Domain Features

Barman

Kishor

Raju

et al. 2021

Advanced Computing and Intelligent Technologies

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Content-Based Image Retrieval (CBIR) has become one of the trending areas of research in computer vision. In traditional CBIR, the features in a spatial domain, such as color, texture, shape, and point features, are extracted. It is often considered that apart from the spatial features, the features extracted from the frequency domain of the images can give further information on the features of an image. In this paper, we are proposing two novel methods for the purpose of feature extraction from the 2-dimensional Discrete Cosine Transform (DCT) of an image: DCT_256_Zigzag and DCT_256_2 × 2. These methods take into consideration the lower frequencies in order to determine the features in the frequency domain. The advantage of using the zigzag scanning is to have the maximum low frequency values having Higher Energies comparatively. These two features are combined with two of the existing spatial domain features: Local Binary Patterns (LBP) and Interchannel voting features to generate a global feature vector for an image. For a query image, its feature vector is compared with feature vectors of every other image in the database using d1-distance, and the images with least distance are considered most similar images to the query image. To evaluate the efficiency of these two methods, five standard performance measures such as Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are used. Six benchmark image datasets Corel-1000, Corel-5000, Corel-10000, VisTex, STex, and Color-Brodatz are used to corroborate the performance of these methods.

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Section: Methodsmentioning

confidence: 99%

“…In [16][17][18], different image retrieval methods have been covered and discussed. Suresh et al [19] proposed a new color feature named interchannel voting among the three components of a HSI image. This method explores the interrelationship among three components: Hue (H), Saturation (S), and Intensity (I) of a color image.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval Using Energy-Based Frequency Domain Features

Barman

Kishor

Raju

et al. 2021

Advanced Computing and Intelligent Technologies

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“…proposed a new color feature named inter-channel voting among hue, saturation, and intensity components of an HSI color image. 67 This method explores the interrelationship among three components of a color image. To perform inter-channel voting between saturation and intensity channels, both channels are quantized into different bins and then added with the quantized intensity value to the respective saturation bin and vice versa.…”

Section: Interchannel Votingmentioning

confidence: 99%

Content‐based image retrieval for super‐resolutioned images using feature fusion: Deep learning and hand crafted

Pathak

Raju

2022

Concurrency and Computation

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An innovative image retrieval agenda by concatenating deep learning features from GoogleNet and low‐level features from HSI and RGB color space is proposed in this article. Most of the CNN features suffer from loss of information due to image resize as a pre‐processing stage. To reduce this information loss super‐resolution technic is used for resizing images. An improved form of dot‐diffused block truncation coding is used for extracting RGB handcraft features. To discover the interdependencies between color and intensity component of an image, interchannel voting between hue, saturation, and intensity component is calculated as a color feature in HSI space. Histogram of orientated gradient (HOG) feature is used as shape feature. Five standard performance parameters, average precision rate, average recall rate, F‐Measure, Average Normalized Modified Retrieval Rank, and Total Minimum Retrieval Epoch, are applied on nine image datasets: Corel‐1K, Corel‐5K, Corel‐10K, VisTex, STex, ColorBrodatz and three subsets of ImageNet dataset for evaluation process of proposed method. For all dataset the best performance is achieved by the proposed method with respect to all performance parameters.

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“…Here, a series of extensive experiments are conducted to compare our method with other leading edge ones, namely, traditional hand craft-based methods [22][23][24][25] and CNN-based methods [26][27][28][29][30]. To evaluate the performance, we use the average precision (AP) measure computed as the area under the precision-recall curve for a query.…”

Section: Experiments On Image Retrievalmentioning

confidence: 99%

Accurate Quaternion Polar Harmonic Transform for Color Image Analysis

Zhang¹,

Sang

Dai³

2021

Scientific Programming

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Polar harmonic transforms (PHTs) have been applied in pattern recognition and image analysis. But the current computational framework of PHTs has two main demerits. First, some significant color information may be lost during color image processing in conventional methods because they are based on RGB decomposition or graying. Second, PHTs are influenced by geometric errors and numerical integration errors, which can be seen from image reconstruction errors. This paper presents a novel computational framework of quaternion polar harmonic transforms (QPHTs), namely, accurate QPHTs (AQPHTs). First, to holistically handle color images, quaternion-based PHTs are introduced by using the algebra of quaternions. Second, the Gaussian numerical integration is adopted for geometric and numerical error reduction. When compared with CNNs (convolutional neural networks)-based methods (i.e., VGG16) on the Oxford5K dataset, our AQPHT achieves better performance of scaling invariant representation. Moreover, when evaluated on standard image retrieval benchmarks, our AQPHT using smaller dimension of feature vector achieves comparable results with CNNs-based methods and outperforms the hand craft-based methods by 9.6% w.r.t mAP on the Holidays dataset.

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Image Retrieval by Integrating Global Correlation of Color and Intensity Histograms with Local Texture Features

Cited by 22 publications

References 43 publications

Content-Based Image Retrieval Using Energy-Based Frequency Domain Features

Content-Based Image Retrieval Using Energy-Based Frequency Domain Features

Content‐based image retrieval for super‐resolutioned images using feature fusion: Deep learning and hand crafted

Accurate Quaternion Polar Harmonic Transform for Color Image Analysis

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