Columbia Object Image Library Research Articles

BENDLET TRANSFORM BASED OBJECT DETECTION SYSTEM USING PROXIMITY LEARNING APPROACH

This study presents a Bendlet Transform-based Object Detection (BTOD) system that recognizes an object in the image. Finding a specific object in images or videos is the goal of the field of object recognition. Though humans are able to identify a large number of objects, it is very difficult for computer vision systems in general. The appearance of the objects may change depending on the perspective, the size or scale, or translation and rotation. This work extracts Bendlet transform-based features from the images at different levels, and then the discriminant features are selected by employing genetic algorithms. The performance of the BTOD system is analyzed with different nearest neighbours for classifying objects in the Columbia Object Image Library (COIL-100) in terms of classification accuracy. It is observed from the results that the BTOD system with a one-nearest neighbour provides better performance than the two-nearest neighbour classifier. The former classifier gives 99.47% accuracy, whereas the later classifier gives 99.19%.  

Color Based Object Categorization Using Histograms of Oriented Hue and Saturation

In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.

Adapted Jacobi Orthogonal Invariant Moments for Image Representation and Recognition

Images recognition and classification require an extraction technique of feature vectors of these images. These vectors must be invariant to the three geometric transformations: rotation, translation and scaling. Several authors used the theory of orthogonal moments to extract the feature vectors of images. Jacobi moments are orthogonal moments, which have been widely applied in imaging and pattern recognition. However, the invariance to rotation of Cartesian Jacobi moments is very difficult to obtain. In this paper, we obtain at first a set of transformed orthogonal Jacobi polynomials, called “Adapted Jacobi polynomials”. Based on these polynomials, a set of orthogonal moments is presented, named adapted Jacobi moments (AJMs). These moments are orthogonal on the rectangle $$\left[ {0, N\left] { \times } \right[0, M} \right],$$ where $$N \times M$$ is the size of the described image. We also provide a new series of feature vectors of images based on adapted Jacobi orthogonal invariants moments, which are a linear combination of geometric moment invariants, where the latest ones are invariant under rotation, translation and scaling of the described image. Based on k-NN algorithm, we apply a new 2D image classification system. We introduce a set of experimental tests in pattern recognition. The obtained results express the efficiency of our method. The performance of these feature vectors is compared with someones extracted from Hu, Legendre and Tchebichef invariant moments using three different 2D image databases: MPEG7-CE shape database, Columbia Object Image Library (COIL-20) database and ORL database. The results of the comparative study show the performance and superiority of our orthogonal invariant moments.

New set of adapted Gegenbauer–Chebyshev invariant moments for image recognition and classification

Orthogonal moments and their invariants to geometric transformations for gray-scale images are widely used in many pattern recognition and image processing applications. In this paper, we propose a new set of orthogonal polynomials called adapted Gegenbauer–Chebyshev polynomials (AGC). This new set is used as a basic function to define the orthogonal adapted Gegenbauer–Chebyshev moments (AGCMs). The rotation, scaling, and translation invariant property of (AGCMs) is derived and analyzed. We provide a novel series of feature vectors of images based on the adapted Gegenbauer–Chebyshev orthogonal moments invariants (AGCMIs). We practice a novel image classification system using the proposed feature vectors and the fuzzy k-means classifier. A series of experiments is performed to validate this new set of orthogonal moments and compare its performance with the existing orthogonal moments as Legendre invariants moments, the Gegenbauer and Tchebichef invariant moments using three different image databases: the MPEG7-CE Shape database, the Columbia Object Image Library (COIL-20) database and the ORL-faces database. The obtained results ensure the superiority of the proposed AGCMs over all existing moments in representation and recognition of the images.

New Set of Non-separable Orthogonal Invariant Moments for Image Recognition

It is known that the rotation, scaling and translation invariant property of image moments has a high significance in image recognition. For this reason, the seven invariant moments presented by Hu are widely used in the field of image analysis. These moments are of finite order; therefore, they do not comprise a complete set of image descriptors. For this reason, we introduce in this paper another series of invariant moments of infinite order, which are based on normalized central moments. The non-orthogonal property of these moments causes the redundancy of information. To overcome this problem, we propose a new construction technique of non-separable orthogonal polynomials in two variables based on a recurrence formula and we present a new set of orthogonal moments, which are invariant to translation, scaling and rotation. The presented approaches are tested in several well-known computer vision datasets including moment’s invariability, image retrieval and classification of objects, this latter based on fuzzy K-means clustering algorithm. The performance of these invariant moments for classification and image retrieval is compared with some recent invariant moments such as invariants of multi-channel orthogonal radial-substituted Chebyshev moments, invariants of quaternion radial-substituted Chebyshev moments, invariants of rotational moments in Radon space and Legendre–Fourier moments in Radon space. The experimental results made using four databases of images, namely Columbia Object Image Library (COIL-20) database, MPEG7-CE shape database, COIL-100 database and ORL database, show that our orthogonal invariant moments have done better than the other descriptors tested.

Rotation scaling and translation invariants by a remediation of Hu’s invariant moments

Due to the invariance to translation, rotation and scaling, the seven invariant moments presented by Hu (Visual pattern recognition by moment invariants, IRE Transactions on Information Theory, vol. 8, February 1962, pp. 179–187) are widely used in the field of pattern recognition. The set of these moments is finite; therefore, they do not comprise a complete set of image descriptors. To solve this problem, we introduce in this paper a new set of invariant moments of infinite order. The non-orthogonal property causes the redundancy of information. For this reason, we propose a new set of orthogonal polynomials in two variables, and we present a set of orthogonal moments, which are invariant to rotation, scale and translation. The presented approaches are tested by the invariability of the moments, the image retrieval and the classification of the objects. In this framework, using the proposed orthogonal moments, we present two classification systems. The first based on the Fuzzy C-Means Clustering algorithm (FCM) and the second based on the Radial Basis Functions Neural Network (RBF). The performance of our invariant moments is compared with Legendre invariant moments, Tchebichef-Krawtchouk (TKIM), Tchebichef-Hahn (THIM), Krawtchouk-Hahn (KHIM), Hu invariant moments, the descriptor of histogram of oriented gradients (HOG), the adaptive hierarchical density histogram features (AHDH) and with descriptors of color and texture Hist, HSV, FOS and SGLD. The experimental tests are performed on seven image databases: Columbia Object Image Library (COIL-20) database, MPEG7-CE shape database, MNIST handwritten digit database, MNIST fashion image database, ImageNet database, COIL-100 database and ORL database. The obtained results show the efficiency and superiority of our orthogonal invariant moments.

Deep Image Sensing and Retrieval Using Suppression, Scale Spacing and Division, Interpolation and Spatial Color Coordinates With Bag of Words for Large and Complex Datasets

Intelligent and efficient image retrieval from versatile image datasets is an inevitable requirement of the current era. Primitive image signatures are vital to reflect the visual attributes for content based image retrieval (CBIR). Algorithmically descriptive and well identified visual contents form the image signatures to correctly index and retrieve similar results. Hence feature vectors should contain ample image information with color, shape, objects, spatial information perspectives to distinguish image category as a qualifying candidate. This contribution presents a novel features detector by locating the interest points by applying non-maximum suppression to productive sum of derivative of pixels computed from differential of corner scores. The interest points are described by applying scale space interpolation to scale space division produced from Hessian blob detector resulted after Gaussian smoothing. The computed shape and object information is fused with color features extracted from the spatially arranged L2 normalized coefficients. High variance coefficients are selected for object based feature vectors to reduce the massive data which in fuse form transformed to bag-of-words (BoW) for efficient retrieval and ranking. To check the competitiveness of the presented approach it is experimented on nine well-known image datasets Caltech-101, ImageNet, Corel-10000, 17-Flowers, Columbia object image library (COIL), Corel-1000, Caltech-256, tropical fruits and Amsterdam library of textures (ALOT) belong to shape, color, texture, and spatial & complex objects categories. Extensive experimentation is conducted for seven benchmark descriptors including maximally stable extremal region (MSER), speeded up robust features (SURF), difference of Gaussian (DoG), red green blue local binary pattern (RGBLBP), histogram of oriented gradients (HOG), scale invariant feature transform (SIFT), and local binary pattern (LBP). Remarkable outcomes reported that the presented technique has significant precision rates, recall rates, average retrieval precision & recall, mean average precision & recall rates for many image semantic groups of the challenging datasets. Results comparison is presented with research techniques and reported improved results.

Linear Discriminant Analysis via Pseudo Labels: A Unified Framework for Visual Domain Adaptation

This paper deals with the problem of visual domain adaptation in which source domain labeled data is available for training, but the target domain unlabeled data is available for testing. Many recent domain adaptation methods merely concentrate on extracting domain-invariant features via minimizing the distributional and geometrical divergence between domains simultaneously while ignoring within-class and between-class structure properties, especially for the target domain due to the unavailability of labeled data. We propose Linear Discriminant Analysis via Pseudo Labels (LDAPL), a unified framework for visual domain adaptation that can tackle these two issues together. LDAPL is to learn a domain-invariant features across both domains with preserving important properties such as minimizing the shift between domains both statistically and geometrically, retaining the original similarity of data samples, maximizing the target domain variance, and minimizing the within-class and maximizing the between-class properties of both domains. Specifically, LDAPL preserves the target domain discriminative information (or within-class and between-class properties) using pseudo labels, and these pseudo labels are refined until convergence. Extensive experiment on several visual cross-domain benchmarks, including Office+Caltech10 with all three types of features (such as Speeded Up Robust Features (SURF), Deep Convolutional Activation Feature (DeCAF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> ), and Visual Geometry Group-Fully Connected layer ( VGG-FC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> ) features), COIL20 (Columbia Object Image Library), digit, and PIE (Pose, Illumination, and Expression), LDAPL achieved average accuracies of 79.11%, 99.72%, 79.0%, and 84.50%, respectively. Comparative results on several visual cross-domain classification tasks verify that LDAPL can significantly outperform the state-of-the-art primitive and domain adaptation methods. Specifically, LDAPL gains over baseline Joint Geometrical and Statistical Alignment (JGSA) method with 6.6%, 5.3%, 6.3%, and 44.93% average accuracies on Office+Caltech10 (SURF, DeCAF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> , and VGG-FC6 ), COIL20, digit, and PIE, respectively.

Sparsely encoded distinctive visual features for object recognition

An image feature descriptor named “sparsely encoded distinctive visual features (SEDVF)” is proposed for object recognition. SEDVF is built with the integration of local and global visual features. Visual information on edge orientation, magnitude, color, and pixel intensity is sparsely encoded by a bit plane slicing technique. Distinctive features are obtained using winner-takes-all principle. Edge gradient multiorientation detector method (EGMOD) is proposed to obtain the gradient orientations. EGMOD extracts four-directional (horizontal, vertical, and both diagonal) edge information with the proposed multioriented Scharr operator in YIQ color space. Magnitude features are extracted by incorporating chromatic information along horizontal and vertical directions in the RGB color space. SEDVF can be used as a color feature descriptor that has good discriminative power of visual features. The proposed descriptor is extensively tested for performance evaluation using K-nearest neighbor classifier on three standard datasets, including Columbia object image library, Amsterdam library of object images, and PVOC 2007, respectively. Experimental results reveal outperformance of SEDVF as compared to the state-of-the-art object recognition methods.

Implementation of Convolutional Kernel Function Using 3-D TiO&lt;italic&gt;x&lt;/italic&gt; Resistive Switching Devices for Image Processing

We implemented a convolution operation in a convolutional neural network by introducing a 3-D integrated TiO x -based resistive switching device (RSD) array to act as the kernel. The 3-D integrated TiO x -based RSD exhibits a gradual SET/RESET behavior, which enables multilevel characteristics. A pair of layers in the 3-D structured array is used to achieve the positive and negative weights. Based on these results, we demonstrated the $\textsf {3} \times \textsf {3}$ Laplace kernel for edge detection using images from the Columbia Object Image Library data set in a simulator to validate the convolution operation.

Improved sparse representation method for image classification

Among all image representation and classification methods, sparse representation has proven to be an extremely powerful tool. However, a limited number of training samples are an unavoidable problem for sparse representation methods. Many efforts have been devoted to improve the performance of sparse representation methods. In this study, the authors proposed a novel framework to improve the classification accuracy of sparse representation methods. They first introduced the concept of the approximations of all training samples (i.e., virtual training samples). The advantage of this is that the application of virtual training samples can allow noise in original training samples to be partially reduced. Then they proposed an efficient and competent objective function to disclose more discriminant information between different classes, which is very significant for obtaining a better classification result. The devised sparse representation method employs both the original and virtual training samples to improve the classification accuracy since the two kinds of training samples makes sample information to be fully exploited in a good way, also satisfactory robustness to be obtained. The experimental results on the JAFFE, ORL, Columbia Object Image Library (COIL‐100) AR and CMU PIE databases show that the proposed method outperforms the state‐of‐art image classification methods.

Experimental analysis of SIFT and SURF features for multi-object image retrieval

Content-based image retrieval (CBIR) is the most challenging task of retrieving similar images for large databases. In this work, we present experimental analysis based on scale invariant feature transform (SIFT) and speeded up robust features (SURF) as local features for multi-object image retrieval. The experiments are conducted on database consisting of group of images obtained by aggregating two objects, three objects, four objects and five objects together from the Columbia Object Image Library (COIL-100) image dataset of ten categories. For the query image, features are extracted using SIFT or SURF. These features of query image are compared with corresponding features stored in the database for similarity retrieval. The precision, recall and FMeasure values are computed for each group and results are tabulated. The experimental results show that the accuracy of retrieval from SURF is better than SIFT method.

Experimental analysis of SIFT and SURF features for multi-object image retrieval

Content-based image retrieval (CBIR) is the most challenging task of retrieving similar images for large databases. In this work, we present experimental analysis based on scale invariant feature transform (SIFT) and speeded up robust features (SURF) as local features for multi-object image retrieval. The experiments are conducted on database consisting of group of images obtained by aggregating two objects, three objects, four objects and five objects together from the Columbia Object Image Library (COIL-100) image dataset of ten categories. For the query image, features are extracted using SIFT or SURF. These features of query image are compared with corresponding features stored in the database for similarity retrieval. The precision, recall and FMeasure values are computed for each group and results are tabulated. The experimental results show that the accuracy of retrieval from SURF is better than SIFT method.

A New Efficient Optimal 2D Views Selection Method Based on Pivot Selection Techniques for 3D Indexing and Retrieval

In this paper, we propose a new method for 2D/3D object indexing and retrieval. The principle consists of an automatic selection of optimal views by using an incremental algorithm based on pivot selection techniques for proximity searching in metric spaces. The selected views are afterward described by four well-established descriptors from the MPEG-7 standard, namely: the color structure descriptor (CSD), the scalable color descriptor (SCD), the edge histogram descriptor (EHD) and the color layout descriptor (CLD). We present our results on two databases: The Amsterdam Library of Images (ALOI-1000), consisting of 72,000 color images of views, and the Columbia Object Image Library (COIL-100), consisting of 7200 color images of views. The results prove the performance of the developed method and its superiority over the k-means algorithm and the automatic selection of optimal views proposed by Mokhtarian et al.

Object Recognition Based on Empirical Wavelet Transform

Object recognition is the method of finding an object in an image. We recognize objects without any effort easily. It is a challenging task for computer vision systems due to the size, shape, and structure of objects in an image. In this paper, an efficient object recognition system is presented based on Empirical Wavelet Transform (EWT). The energy features obtained form the EWT decomposed image is used as features for the given object. As EWT, a multiresolution analysis, the given image is decomposed at various level of decomposition and the obtained features are analyzed at each level of decomposition. The evaluation of the system is carried on Columbia Object Image Library Dataset (COIL) which consists of 100 objects captured at different orientations. The classification is done with K- nearest neighbor (KNN) which gives 98.42% accuracy.

Content-Based Image Retrieval Using Edge and Gradient Orientation Features of an Object in an Image From Database

AbstractIn this work, we present a combination of edge feature and distribution of the gradient orientation of an object technique for content-based image retrieval (CBIR). First, the bidimensional empirical mode decomposition (BEMD) technique is employed to get the edge features of an image. Later, the information about the gradient orientation is obtained by the histogram of oriented gradient (HOG) descriptor. These two features are extracted from the images and stored in the database for further usage. When the user submits the query image, the features are extracted in same way and compared with the features of the data set images. Based on the similarity, the relevant images have been selected as a resultant set. These images are ranked from higher similarity to lower similarity and displayed on the user interface. The experiments are carried out using the Columbia Object Image Library (COIL-100) dataset. The COIL-100 database is a collection of 7200 color images belonging to 100 various objects, each with 72 different orientations. Our proposed method results are high with precision and recall values of 93.00 and 77.70, respectively. Taken individually, the precision and recall values for BEMD are 82.25 and 68.54 and for HOG are 85.00, 71.10, respectively. The observation from the experimental result is that the combined method performs better than the individual methods. Experiments are conducted in the presence of noise, and the robustness of the method is verified.

Sparse distributed localized gradient fused features of objects

The sparse, hierarchical, and modular processing of natural signals is related to the ability of humans to recognize objects with high accuracy. In this study, we report a sparse feature processing and encoding method, which improved the recognition performance of an automated object recognition system. Randomly distributed localized gradient enhanced features were selected before employing aggregate functions for representation, where we used a modular and hierarchical approach to detect the object features. These object features were combined with a minimum distance classifier, thereby obtaining object recognition system accuracies of 93% using the Amsterdam library of object images (ALOI) database, 92% using the Columbia object image library (COIL)-100 database, and 69% using the PASCAL visual object challenge 2007 database. The object recognition performance was shown to be robust to variations in noise, object scaling, and object shifts. Finally, a comparison with eight existing object recognition methods indicated that our new method improved the recognition accuracy by 10% with ALOI, 8% with the COIL-100 database, and 10% with the PASCAL visual object challenge 2007 database.

Object Recognition Based on Wave Atom Transform

This study presents an efficient method for recognizing object in an image based on Wave Atom Trans-form (WAT). Object recognition is achieved by extracting the energies from all coefficients of WAT. The original image is decomposed by using the WAT. All coefficients are considered as features for the classification process. The extracted features are given as an input to the K-Nearest Neighbor (K-NN) classifier to recognize the object. The evaluation of the system is carried on using Columbia Object Image Library Dataset (COIL-100) database. The classification performance of the proposed system is evaluated by using classification rate in percentage, which is achieved by varying the angle between the views.

Object Recognition Based on Dual Tree Complex Wavelet Transform

Automated recognition of objects from images plays an important role in many computer vision systems such as robot navigation, object manipulation and content based image retrieval. In this study, an approach for object recognition based on Dual Tree Complex Wavelet Transform (DTCWT) is proposed. The proposed approach attempts to extract the detailed information of objects from the multi scale representation by DTCWT. The proposed system is tested on Columbia Object Image Library (COIL-100). All the objects are considered for the classification based on nearest neighbor classifier. The results show that the maximum recognition accuracy achieved by the proposed approach is 97.03%.

A method for preserving the classifiability of digital images after performing a wavelet-based compression

A measure is introduced that predicts the number of coefficients needed to be retained in the discrete wavelet transform of images in order to maintain their classifiability. The introduction of the criterion is based on the energy content of the wavelet coefficients and the order in which they are scanned. The coefficients are weighted based on their location acquired by Morton scanning of the two-dimensional transform plane. The proposed criterion has been tested on MIT-CBCL and AT&T-Olivetti face databases, Columbia Object Image Library (COIL-20) object database, the MNIST handwritten character recognition database and on Caltech-101 object image database. To demonstrate the efficiency of the proposed method, several classification experiments are conducted on each database. Simulation results show that the proposed method can maintain the same classifiability as that of uncompressed data with only a small fraction of the wavelet coefficients.