Image classification by addition of spatial information based on histograms of orthogonal vectors

Abstract

The Bag-of-Visual-Words (BoVW) model is widely used for image classification, object recognition and image retrieval problems. In BoVW model, the local features are quantized and 2-D image space is represented in the form of order-less histogram of visual words. The image classification performance suffers due to the order-less representation of image. This paper presents a novel image representation that incorporates the spatial information to the inverted index of BoVW model. The spatial information is added by calculating the global relative spatial orientation of visual words in a rotation invariant manner. For this, we computed the geometric relationship between triplets of identical visual words by calculating an orthogonal vector relative to each point in the triplets of identical visual words. The histogram of visual words is calculated on the basis of the magnitude of these orthogonal vectors. This calculation provides the unique information regarding the relative position of visual words when they are collinear. The proposed image representation is evaluated by using four standard image benchmarks. The experimental results and quantitative comparisons demonstrate that the proposed image representation outperforms the existing state-of-the-art 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].…”

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].…”

An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features

“…The fusion of low-level features is used to boost the performance of image retrieval as single feature-based image representation cannot handle the variety of changes/transformations that are in the multimedia archives [7][8][9][10]. The similarity measure is applied in CBIR to sort the similar images and selection of appropriate similarity measure affects the performance of CBIR [5].…”

“…The appropriate selection of two low-level features can increase the effectiveness of CBIR because this process integrates different feature spaces [8]. Here it is necessary to mention that the proper selection of features to be use in fusion is important as fusion of inappropriate feature spaces degrades the performance of CBIR [5,6,8]. Due to these facts, in this paper, we aim to investigate the low-level feature fusion of color and shape features ( color histogram [12] , color moments [13,14] and invariant moments [15]) as these features are reported intuitive, compact and robust for image representation, rotations and translation [3,16].…”

“…6. For image retrieval process, the query image is taken and steps are repeated from [1][2][3][4][5] to compute a feature vector on the basis of combination of low-level features consisting of color and shape. The similarity among the images is computed through Manhattan distance to sort the similar images from the database.…”