# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 1 Introduction
Inspired by its recognition accuracy and simplicity, several variants of LBPs have been proposed. The local tetra patterns (LTrPs) [9] encode the relationship between the center pixel and its neighbors by using first-order derivatives in vertical and horizontal directions. The directional local extrema patterns (DLEP) [10] extract directional edge information based on local extrema in 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) directions in an image. In addition, CBIR methods derived from wavelet-based texture features from Gabor wavelets [11], discrete wavelet transforms (DWT) [12], DTCWT [13], and shape-adaptive discrete wavelet transforms (SA-DWT) [14] have been studied. SA-DWT can work on each image region separately and preserve its spatial and spectral properties. The extraction of shape features is mainly performed to capture the shape attributes (such as bending moment, area and boundary, etc.) of an image item. Efficient shape features must present essential properties such as identifiability, translation, rotation, scale-invariance, affine invariance, noise resistance, occultation independence, and reliability [15]. Shape-based methods are grouped into region-based and counter-based techniques. Methods such as Fourier descriptors [16] and geometric moments [17] are often used. Local descriptors such as scale-invariant feature transform (SIFT) [18], speeded up robust features (SURF) [19], histograms of oriented gradient (HOG) [20], gist scene descriptors [21], and so on, are also available for image retrieval. These descriptors are mainly invariant to geometrical transformations but have high computational complexity. Some ways to extract image feature descriptors are by using a compression scheme. The vector quantization (VQ) [22] works by dividing a large set of vectors into groups with approximately the same number of points closest to them. Quantization introduces two problems: information about the original descriptor is lost, and corresponding descriptors may be assigned to different visual words [23]. The ordered dither block truncation coding (ODBTC) [24] compresses an image block into corresponding quantizers and bitmap images. The dither array in the ODBTC method substitutes the fixed average value as the threshold value for bitmap image generation. Image features can be roughly divided into two categories according to the ability to describe image semantics: shallow and deep features. Image retrieval based on deep features has become a hot research field with increased image data sets. Deep learning techniques allow a system to learn features with a CNN architecture. CNN-based techniques are also incorporated to extract image features at various scales and are encoded using a bag of words (BoW) [25] or vector of locally aggregated descriptors (VLAD) [26]. Recently, some CNN-based CBIR methods have been proposed. ENN-BR [27] embedded neural networks with band letized regions. LeNet6 [28] extracted the fully connected F6 of the LeNet network [29] as the feature; shape-based filtering. SBF-SMI-CNN [30] integrated spatial mapping with CNN. The dimension reduction-based methods [31] use multilinear principal component analysis (MPCA) to reduce the dimensionality of image features. The use of a shallow function to distinguish between different images has some limitations. Therefore, many methods based on the fusion of several shallow features have been proposed, such as CCM and the difference between pixels in a scan pattern (CCM-DBPSP) [32], color histogram and local directional pattern (CH-LDP) [33], color-shape, and a bag of words (C-S-BOW) [34], microstructure descriptor and uniform local binary patterns (MSD-LDP) [35], and correlated microstructure descriptor (CMSD) [36]. CMSD is used for correlating color, texture orientation, and intensity information. The fused information feature-based image retrieval system (FIF-IRS) [37] is composed of an eight-directional gray level co-occurrence matrix (8D-GLCM) and HSV color moments (HSVCM) [38].