Intelligent image classification-based on spatial weighted histograms of concentric circles

Abstract

As digital images play a vital role in multimedia content, the automatic classification of images is an open research problem. The Bag of Visual Words (BoVW) model is used for image classification, retrieval and object recognition problems. In the BoVW model, a histogram of visual words is computed without considering the spatial layout of the 2-D image space. The performance of BoVW suffers due to a lack of information about spatial details of an image. Spatial Pyramid Matching (SPM) is a popular technique that computes the spatial layout of the 2-D image space. However, SPM is not rotation-invariant and does not allow a change in pose and view point, and it represents the image in a very high dimensional space. In this paper, the spatial contents of an image are added and the rotations are dealt with efficiently, as compared to approaches that incorporate spatial contents. The spatial information is added by constructing the histogram of circles, while rotations are dealt with by using concentric circles. A weighed scheme is applied to represent the image in the form of a histogram of visual words. Extensive evaluation of benchmark datasets and the comparison with recent classification models demonstrate the effectiveness of the proposed approach. The proposed representation outperforms the state-of-the-art methods in terms of classification accuracy.

An efficient content based image retrieval using enhanced multi-trend structure descriptor

“…Concurrently, Gradient location and orientation histogram with PCA is used in Mikolajczyk and Schmid [39] for retrieving the images; Liu and Yang [40] computes the spatial correlation of textons using texton co-occurrence matrices (TCM) which extracts energy, entropy, contrast and homogeneity to represent the image; attributes of cooccurrence matrix is expressed using histogram based on Julesz's textons theory for analyzing the natural images in Liu et al [41] and is named as multi-texton histogram (MTH), and the authors confirmed that their approach achieves better performance than the texton co-occurrence matrix and edge orientation auto-correlogram; feature based on edge orientation similarity and underlying colors is described by Liu et al [42] and named it as Micro-Structure Descriptor (MSD) which captures local level color and texture effectively; Saliency Structure Histogram (SSH) reported in Liu and Yang [43] computes the logarithm characteristics of Gabor energy to describe the image; Structure Element Descriptor (SED) comprising of color and texture information and Structure Element Histogram (SEH) comprising of the spatial correlation of color and texture feature is reported in Xingyuan and Zongyu [44] for image retrieval; Seetharaman and Sathiamoorthy [45] introduced a new variant of EOAC in which edges are identified in HSV color space using a framework based on Full Range Gaussian Markov Random Field (FRGMRF) model that extracts very minute and fine edges from HSV color space and evades loss of edges owing to spectral variations; Gradient field histogram of gradient (GF-HOG) is reported for the retrieval of photo collections [46] and it attains better results than the features like multi-resolution HOG, SIFT, structure tensor, etc. Histograms of triangular regions and relative spatial information for histogrambased representation of the BoVW (Bag of visual words) model are reported in Ali et al [23,24] and Zafar et al [47][48][49] respectively. Feature computation based on spatial information is reported in Zafar et al [47][48][49], Latif et al [50] and Ali et al [51].…”

“…Histograms of triangular regions and relative spatial information for histogrambased representation of the BoVW (Bag of visual words) model are reported in Ali et al [23,24] and Zafar et al [47][48][49] respectively. Feature computation based on spatial information is reported in Zafar et al [47][48][49], Latif et al [50] and Ali et al [51].…”

Building multimedia repository for composing images perspective

“…The system uses only cartoon images and disregards essential educational be benefits from other multimedia types. And comparatively other research worked on object classification and recognition in images, in particular face recognition are investigated recently in [38][39][40]. These works propose new face registration algorithm and extends well known face recognition approaches based on face classification, identification and verification techniques.…”

Estimation of gait normality index based on point clouds through deep auto-encoder

“…Recently, such approaches have demonstrated good performance on common problems such as content-based image retrieval [2,42] and image classification [3,40,41]. Another approach is the use of pretrained deep networks for feature extraction such as [27,31].…”