Robust Affine Invariant Shape Descriptors

With the increasing number of available digital images, there is an urgent need of image content description to facilitate content based image retrieval (CBIR). Besides colour and texture, shape is an important low level feature in describing image content. An object can be photographed from different distances and angles. However, we often want to classify the images of the same object into one class, despite the change of perspective. So, it is desired to extract shape features that are invariant to the change of perspective. The shape of an object from one viewpoint to another can be linked through an affine transformation, if it is viewed from a much larger distance than its size along the line of sight. Those invariant shape features are known as affine invariant shape representations. Because of the change of perspective, it is more difficult to develop affine invariant shape representations than normal ones. The goal of this work is to develop affine invariant shape descriptors. Through shape retrieval experiments, we find that the performance of the existing affine invariant shape representations are not satisfactory. Especially, when the shape boundary is corrupted by noise, their performance degrades quickly. In this work, two new affine invariant contour-based shape descriptors, the ICA Fourier shape descriptor (ICAFSD) and the whitening Fourier shape descriptor (WFSD) have been developed. They perform better than most of the existing affine invariant shape representations, while having compact feature size and low computational time requirement. Four region-based affine-invariant shape descriptors, the ICA Zernike moment shape descriptor (ICAZMSD), the whitening Zernike moment shape descriptor (WZMSD), the ICA orthogonal Fourier Mellin moment shape descriptor (ICAOFMMSD), and the whitening orthogonal Fourier Mellin moment shape descriptor (WOFMMSD), are also proposed, in this work. They can be applied to both simple and complex shapes, and have close to perfect performance in retrieval experiments. The advantage of those newly proposed shape descriptors is even more apparent in experiments on shapes with added boundary noise: Their performance does not deteriorate as much as the existing ones.

Creation of a content-based image retrieval system implies solving a number of difficult problems, including analysis of low-level image features and construction of feature vectors, multidimensional indexing, design of user interface, and data visualization. Quality of a retrieval system depends, first of all, on the feature vectors used, which describe image content. The paper presents a survey of common feature extraction and representation techniques and metrics of the corresponding feature spaces. Color, texture, and shape features are considered. A detailed classification of the currently known features' representations is given. Experimental results on efficiency comparison of various methods for representing and comparing image content as applied to the retrieval and classification tasks are presented.

The aim of this study was to evaluate different shape descriptors applied to images of polygons that represent the organization of football teams on the pitch. The effectiveness of different shape descriptors (area/perimeter, fractal area, circularity, maximum fractal, rectangularity, multiscale fractal curve—MFC), and the concatenation of all shape descriptors (except MFC), denominated Alldescriptors (AllD)) was evaluated and applied to polygons corresponding to the shapes represented by the convex hull obtained from players’ 2D coordinates. A content-based image retrieval system (CBIR) was applied for 25 users (mean age of 31.9 ± 8.4 years) to evaluate the relevant images. Measures of effectiveness were used to evaluate the shape descriptors (P@n and R@n). The MFD (P@5, 0.46±0.37 and P@10, 0.40±0.31, p < 0.001; R@5, 0.14±0.13 and R@10, 0.24±0.19, p < 0.001) and AllD (P@5 = 0.43±0.36 and P@10 = 0.39±0.32, p < 0.001; R@5 = 0.13±0.11 and R@10 = 0.24±0.20, p < 0.001) descriptors presented higher values of effectiveness. As a practical demonstration, the best evaluated shape descriptor (MFC) was applied for tactical analysis of an official match. K-means clustering technique was applied, and different shapes of organization could be identified throughout the match. The MFC was the most effective shape descriptor in relation to all others, making it possible to apply this descriptor in the analysis of professional football matches.

In this paper, a novel technique for matching images of object shapes which have been subject to affine transformation caused by variations in the camera position is reported. The method is based on the genetic algorithm, and is more efficient and reliable than conventional approaches that rely on corresponding dominant point pairs to determine the best alignment between object boundaries. Experimental results are presented to demonstrate the feasibility of the approach and its capability in identifying object shapes that had been distorted with heavy noise contamination

Content Based Image Retrieval (CBIR) is important in computer aided plant species recognition. Texture and shape information have been the primitive image descriptors in content based image retrieval systems. This paper presents a novel framework for combining both the features, texture and shape information, and achieve higher retrieval efficiency. The study provides a methodology for retrieving medicinal plants images from a database of medicinal plant images based on shape and texture features. The shape descriptors include Zernike moment, Fourier descriptor (FD), Generic fourier Descriptor(GFD) and for texture descriptors gabor filters are used. The similarity measures, euclidean distance of each medicinal plant image from the database to query image is used. The images are sorted based on similarity of Euclidean distance. The retrieval experiments are carried on different training and test medicinal plant images. The effectiveness of different descriptors is confirmed by the experimental results. We have investigated shape and texture features for medicinal plant retrieval by successively combining the different transforms. The retrieval efficiency is reported through precision and recall rate. Experimental results by combining Gabor and Zernike transform outperforms the all other methods.

We design a generic contrast and affine invariant planar shape recognition algorithm. By generic, we mean an algorithm which delivers a list of all shapes two digital images have in common, up to any affine transform or contrast change. Affine invariance must not be considered as a weak requirement in shape recognition since it is well known that if 3D objects with smooth boundaries are being observed, then the deformation of the objects’ images, far enough from the occlusion boundaries, is locally an affine transform. We define as ”shape elements” all pieces of level lines of the image. We then discuss an efficient local encoding of the shape elements. We finally show experiments. Applications aimed at include image registration, image indexing, optical flow.

We address the problem of texture segmentation by using a novel affine invariant model. The introduction of affine invariance as a requirement for texture analysis goes beyond what is known of the human performance and also beyond the psychophysical theories. We propose to compute texture features using affine invariant intrinsic neighborhoods and affine invariant intrinsic orientation matrices. We discuss several possibilities for the definition of the channels and give comparative experimental results where an affine invariant Mumford-Shah type energy functional is used to compute the multichannel affine invariant segmentation. We prove that the method is able to retrieve faithfully the texture regions and to recover the shape from texture information in images where several textures are present. The numerical algorithm is multiscale.

We design a generic contrast and affine invariant planar shape recognition algorithm. By generic, we mean an algorithm which delivers a list of all shapes two digital images have in common, up to any affine transform or contrast change. We define as“shape elements” all pieces of level lines of the image. Their number can be drastically reduced by using affine and contrast invariant smoothing Matheron operators, which we describe as alternate affine erosions-dilations. We then discuss an efficient local encoding of the shape elements. We finally show experiments. Applications aimed at include image registration, image indexing, optical flow.Key wordsShape RecognitionContrast and Affine InvariancePartial Occlusion

A novel summation invariant feature under transformation group action for 3D surface recognition is proposed, and its application to 3D face recognition is investigated. Based on a systematic mathematical procedure called moving frame, we derived the summation invariant feature that is invariant under affine transformation. Compared with classical differential invariants, such as the mean curvature or the Gaussian curvature, summation invariant feature is far less sensitive to observation noise in the data. A further enhancement leads to a new type of invariant 3D surface shape descriptor called a semi-local summation invariant. We demonstrate one important, potential application of this new feature to 3D human face recognition

Motion trajectory recognition is important for characterizing the moving property of an object. The speed and accuracy of trajectory recognition rely on a compact and discriminative feature representation, and the situations of varying rotation, scaling and translation has to be specially considered. In this paper we propose a novel feature extraction method for trajectories. Firstly a trajectory is represented by a proposed point context, which is a rotation-scale-translation (RST) invariant shape descriptor with a flexible tradeoff between computational complexity and discrimination, yet we prove that it is a complete shape descriptor. Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space. Experimental results show that, the proposed trajectory feature shows encouraging improvement than state-of-art methods.

Existing automatic target recognition of synthetic aperture radar (SAR ATR) schemes mainly focus on target chips, but there is very little research for a large-scale and high-resolution SAR image that is more practical for SAR image interpretation. How to recognize targets efficiently and accurately from a large-scale and high-resolution SAR image is still a challenge. We present a scheme based on the combination of a salient detection approach, an active contour model (ACM), an affine-invariant shape descriptor, and the corresponding shape context. During the detection stage, the spectral residual approach is utilized to efficiently preselect salient regions. The proposed convex ACM, based on a ratio distance and distribution metric which makes it more robust to multiplicative speckled noise, is then adopted to get accurate candidate target chips. For the discrimination stage, a cumulative sum of multiscale lacunarity feature is proposed to select vehicle chips from clutter chips. Finally, affine-invariant shape features, obtained from the contours by our proposed ACM, are combined with a corresponding shape context to make the classification more accurate. Experimental results demonstrate that our SAR ATR system, integrating all the proposed methods, is feasible in ATR from a high-resolution and large-scale SAR image.

This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems.

The shape is one of the most significant properties used in Content Based Image Retrieval (CBIR), however for a CBIR shape representation should satisfy several properties such as rotation invariance, robustness, in this article we are trying to study and combine two shape descriptors: the shape descriptor based on the area of MPEG-7, It is defined on a unit disk in polar coordinates and the shape descriptor based outline of MPEG-7 is based on the representation of the contour scale-space curvature (CSS).

Image annotation and retrieval has been a popular research topic for decades. Based on published journals from 2012 until 2015, a lot of research and studies has been focused on Content Based Image Retrieval (CBIR). In most cases, CBIR systems that use an image as the input query always face a problem called semantic gap due to the use of low-level features for similarity matching. The semantic gap will consequently reduce the performance of the CBIR systems. To overcome this problem, a new class of CBIR known as Annotation Based Image Retrieval (ABIR) has been developed. The ABIR systems, based on the annotation process of the images, employ bag of words for query. However it is found that employing pure bag of word only is not adequate to solve semantic problem. In this study, an attempt to use a Gaussian Mixture Model (GMM) based approach and spatial related information of the annotated objects has been performed in order to improve the performance of the ABIR systems. From the retrieval experiments, it is found that ABIR could achieve average performance up to 88% which is two times better than the CBIR systems in reducing the semantic problem.

Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database.Image contents are plays significant role for image retrieval.There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design.Each image has three contents such as: color, texture and shape features.Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results.Color histogram and texture features have potential to retrieve similar images on the basis of their properties.As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query.To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solution.