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

Abstract

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.

Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion

“…Pradhan, J., et al [7] proposed a regions-of-attention-based feature fusion technique for image retrieval, here authors used multi-directional texture features with spatial correlation-based color features to derive the image semantics. Pathak, D., et al [9] The CRMLBP is computed for the sign-difference, magnitudedifference, and central gray value patterns in the RGB color space and their histograms are concatenated. The feature weights are optimized using Particle Swam Optimization (PSO) technique.…”

“…The efficiency of the CBIR system greatly depends upon the visual feature selection. The high-level semantic features [3,7] of an image are its color, shape, structure, Zernike values, and histogram are used for manual image annotation and are less biased with noise [8,9]. Features represented using the spatial layout of the pixels within an image patch are referred as low-level features or local descriptors [10][11][12].…”

“…The performance of a unique texture feature varies with different datasets. The major limitation of the texture feature is directly mapping the texture image to its histogram [1,9,[17][18][19], which is represented on a scale of 0 to 255, so that all the information learned from the patches of images are not well preserved. With the implementation of Deep learning features a new breakthrough is achieved in the field of computer vision and its applications.…”

“…The texture feature of an image i.e. the histogram of the texture image is biased by the background image textures, which impedes the learning ability of the classifier [9,17]. Textures of similar images are expected to be alike.…”

Improving Deep-Feature Image Similarity Calculation: A Case Study on an Ukiyo-e Card Matching Game Lottery

“…In addition, their method's performance is sensitive to weight selection. Pathak and Raju [17] fused multiple image features, both non-deep-learning features and deep learning features, and showed that more features in fusion lead to improved performance. However, as with [16], their performance is also sensitive to weight selection.…”