Texture feature is important in describing tire pattern image which provides useful clue in solving crime cases and traffic accidents. In this paper, we propose a novel texture feature extraction method based on HOG (Histogram of Oriented Gradient) and dominant gradient (DG) in tire pattern images, named HOG-DG. The proposed HOG-DG is not only robust to illumination and scale changes but also is rotation-invariant. In the proposed HOG-DG, HOG features are first computed from circular local cells, and HOG features from an image are concatenated and normalized using the DG to construct the HOG-DG feature. HOG-DG is used to train a support-vector-machine (SVM) classifier for tire pattern classification. Experimental results demonstrate its outstanding performance for tire pattern description.

Convolutional Neural Networks are highly effective for image classification. However, it is still vulnerable to image distortion. Even a small amount of noise or blur can severely hamper the performance of these CNNs. Most work in the literature strives to mitigate this problem simply by fine-tuning a pre-trained CNN on mutually exclusive or a union set of distorted training data. This iterative fine-tuning process with all known types of distortion is exhaustive and the network struggles to handle unseen distortions. In this work, we propose distortion robust DCT-Net, a Discrete Cosine Transform based module integrated into a deep network which is built on top of VGG16 [1]. Unlike other works in the literature, DCT-Net is "blind" to the distortion type and level in an image both during training and testing. The DCT-Net is trained only once and applied in a more generic situation without further retraining. We also extend the idea of dropout and present a training adaptive version of the same. We evaluate our proposed DCT-Net on a number of benchmark datasets. Our experimental results show that once trained, DCT-Net not only generalizes well to a variety of unseen distortions but also outperforms other comparable networks in the literature.

We propose a direct approach to learning sparse kernel classifiers for multi-instance (MI) classification to improve efficiency while maintaining predictive accuracy. The proposed method builds on a convex formulation for MI classification by considering the average score of individual instances for bag-level prediction. In contrast, existing formulations used the maximum score of individual instances in each bag, which leads to nonconvex optimization problems. Based on the convex MI framework, we formulate a sparse kernel learning algorithm by imposing additional constraints on the objective function to enforce the maximum number of expansions allowed in the prediction function. The formulated sparse learning problem for the MI classification is convex with respect to the classifier weights. Therefore, we can employ an effective optimization strategy to solve the optimization problem that involves the joint learning of both the classifier and the expansion vectors. In addition, the proposed formulation can explicitly control the complexity of the prediction model while still maintaining competitive predictive performance. Experimental results on benchmark data sets demonstrate that our proposed approach is effective in building very sparse kernel classifiers while achieving comparable performance to the state-of-the-art MI classifiers.

Texture feature plays a vital role in content based Image retrieval (CBIR). Wavelet texture feature modeled by generalized Gaussian density (GGD) [1] performs better than discrete wavelet texture feature. Curve let texture feature was proposed in [2]. In this paper, we compute a new texture feature by applying the generalized Gaussian density to the distribution of curve let coefficients which we call curve let GGD texture feature. The purpose of this paper is to investigate curve let GGD texture feature and compare its retrieval performance with that of curve let, wavelet and wavelet GGD texture features. Experimental results show that both curve let and curve let GGD features perform significantly better than wavelet and wavelet GGD texture features. Among the two types of curve let based features, curve let feature shows better performance in CBIR than curve let GGD texture feature. The findings are discussed in the paper.

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.

In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector produced by compressing the local features. Two variants of naive Bayes classifiers are studied based on the extensions of standard nearest neighbor and support vector machine classifiers. Experimental results have demonstrated superior performance achieved by the proposed naive Bayes classifiers for both music classification and retrieval as compared to the alternative methods.

Effective texture feature is an essential component in any content based image retrieval system. In the past, spectral features, like Gabor and wavelet, have shown superior retrieval performance than many other statistical and structural based features. Recent researches on multi-resolution analysis have found that curvelet captures texture properties, like curves, lines, and edges, more accurately than Gabor filters. However, the texture feature extracted using curvelet transform is not rotation invariant. This can degrade its retrieval performance significantly, especially in cases where there are many similar images with different orientations. This paper analyses the curvelet transform and derives a useful approach to extract rotation invariant curvelet features. Experimental results show that the new rotation invariant curvelet feature outperforms the curvelet feature without rotation invariance.

Feature-based intelligent design and manufacturing systems in the Internet environment are an evolution of traditional geometric and solid modeling systems. This paper presents some novel algorithms including a new face-base representation, composite feature modeling and retrieval technology, and efficient communication mechanism, to construct an interactive framework for composite feature modeling and retrieval. The proposed system consists of a feature modeler developed on Wolfram Research Mathematica, Java and Java 3D enabled GUI (graphical user interface), and DB (database). Experiments demonstrate that this system reflects designers' intent properly and is user-friendly to experts coming from various technical backgrounds. This paper provides some fundamental principles for composite feature modeling and retrieval in web-based distributed environment.

In content based image analysis and retrieval, texture feature is an essential component due to its strong discriminative power. Directionality is one of the most significant texture features which are well perceived by the human visual system. A new method to calculate the directionality of image is proposed in this paper. In contrast to Tamura method which uses the statistical property of the directional histogram of an image to calculate its directionality, the proposed method makes use of the geometric property of the directional histogram. Both subjective and objective analyses prove that the proposed method outperforms the conventional Tamura method. It has also been shown that the proposed directionality has better retrieval performance than the conventional Tamura directionality.

Feature extraction is a key issue in content-based image retrieval (CBIR). In the past, a number of texture features have been proposed in literature, including statistic methods and spectral methods. However, most of them are not able to accurately capture the edge information which is the most important texture feature in an image. Recent researches on multi-scale analysis, especially the curvelet research, provide good opportunity to extract more accurate texture feature for image retrieval. Curvelet was originally proposed for image denoising and has shown promising performance. In this paper, a new image feature based on curvelet transform has been proposed. We apply discrete curvelet transform on texture images and compute the low order statistics from the transformed images. Images are then represented using the extracted texture features. Retrieval results show, it significantly outperforms the widely used Gabor texture feature.

Texture is an important feature to describe images. Though lots of work has been done for efficient texture feature extraction from rectangular images, no much effort has been made in texture feature extraction from arbitrary-shaped regions in region-based image retrieval (RBIR) system. In this paper, we present an efficient texture feature extraction algorithm for arbitrary-shaped regions. This algorithm first extends an arbitrary-shaped region into a rectangular area onto which block transformation can be applied. Based on the projection-onto-convex-sets (POCS) theory, a set of coefficients best describing the original region are finally obtained, from which texture feature of the region can be extracted. Via intensive experiments, we select a set of parameters proper for image retrieval purpose. Experimental results on real-world image database demonstrate the effectiveness of the proposed algorithm for image retrieval purpose

To improve the retrieval accuracy of content-based image retrieval, an important task is to reduce the 'semantic gap' between low-level image features and the richness of human semantics. We present a region-based image retrieval system using high-level semantic concepts. The contribution of the paper is two-fold. First, salient low-level features are extracted from arbitrarily-shaped regions. Second, a fuzzy-ID3 decision tree learning method is proposed to derive association rules which map low-level image features to high-level concepts. Experimental results prove that, by reducing the 'semantic gap', the proposed system not only improves the retrieval accuracy, but also supports users in query-by-keyword.

In this paper, spherical harmonics are proposed as shape descriptors for 2d images. We introduce the concept of connectivity; 2d images are decomposed using connectivity which is followed by 3d model construction. Spherical harmonics are obtained for 3d models and used as descriptors for the underlying 2d shapes. Difference between two images is computed as the Euclidean distance between their spherical harmonics descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2d images. Item S8 within the MPEG-7 Still Images Content Set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the proposed descriptors for 2d images are effective.

Performance of traditional content-based image retrieval systems is far from user’s expectation due to the ‘semantic gap’ between low-level visual features and the richness of human semantics. In attempt to reduce the ‘semantic gap’, this paper introduces a region-based image retrieval system with high-level semantic color names. In this system, database images are segmented into color-texture homogeneous regions. For each region, we define a color name as that used in our daily life. In the retrieval process, images containing regions of same color name as that of the query are selected as candidates. These candidate images are further ranked based on their color and texture features. In this way, the system reduces the ‘semantic gap’ between numerical image features and the rich semantics in the user’s mind. Experimental results show that the proposed system provides promising retrieval results with few features used.

Gabor filters have been proven to be very useful for texture retrieval and are widely adopted (1, 2, 3, 4, 5, 6). However, the original Gabor texture features are rotation variant. Recently, Zhang et al proposed rotation normalization using circular shift. They have shown the proposed rotation normalization is effective through some examples, but did not comprehensively study its performance. The purpose of this paper is to study the performance of the rotation normalization on a good size texture database. Our experimental results show that the proposed rotation normalization is effective in retrieving rotated textures and has some adverse effect on retrieving non-rotated texture.

The texture retrieval approach based on Gabor filters has been shown to be very useful for texture retrieval and is widely adopted. We examine this approach in detail and question if this is a suitable approach. We then propose an alternative approach that does not use filters. Our experimental results and analysis show that our proposed approach performs better than the commonly used approach based on Gabor filters.

Previously, we proposed the concept of connectivity to obtain discriminating shape descriptors. In this paper, we use connectivity to obtain superior distance histograms for multi-scale images. Experiments are performed to evaluate the distance histograms, based on connectivity, for shape-based retrieval of multi-scale images. Item S8 within the MPEG-7 still images content set is used for performing experiments. Experimental results show that the proposed method enhances retrieval performance significantly.

Most content-based image retrievals (CBIR) use color as image features. However, image retrieval using color features often gives disappointing results because in many cases, images with similar colors do not have similar content. Color methods incorporating spatial information have been proposed to solve this problem, however, these methods often result in very high dimensions of features which drastically slow down the retrieval speed. In this paper, a method combining both color and texture features of image is proposed to improve the retrieval performance. Given a query, images in the database are firstly ranked using color features. Then, the top ranked images are re-ranked according to their texture features. Results show the second process improves retrieval performance significantly.

We propose a method for enhancing the accuracy of shape descriptors. The concept of connectivity to obtain discriminating shape descriptors, is introduced. We show how connectivity is applied to two popular shape descriptors. Experiments are performed to test the effect of using connectivity with generic Fourier descriptors and distance histograms. Item S8 within the MPEG-7 still images content set is used for performing experiments. This dataset consists of 3621 still images. The experimental results show that connectivity enhances the performance of the methods significantly.

Gabor filter is widely used to extract texture features from images for image retrieval. A number of parameters (number of scales and orientations and filter mask size) are used in the Gabor filter. In the reported work so far, these parameters seem to be chosen without proper explanations. In this paper, we investigate the effects of different Gabor filter parameters on texture retrieval.

Shape (object) description consists a key part of image content description in MPEG-7. Most of existing shape descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. In this paper, an Enhanced Generic Fourier Descriptor (EGFD) is presented to overcome the drawbacks of existing shape representation techniques. The EGFD is obtained based on our previously proposed Generic Fourier Descriptors (GFD). It is acquired by deriving GFD from the rotation and scale normalized shape. Experimental results show that the proposed EGFD outperforms GFD and Zernike moments descriptors (ZMD) significantly.

Shape (object) description consists a key part of image content description in MPEG-7. In the development of MPEG-7, Zernike moment descriptor (ZMD) has been adopted as region-based shape descriptor. However, because Zernike moments are defined in a unit disk and extracted from shape in polar space, less shape information is used for skewed and stretched shapes. As a result, the extracted ZMD is not robust to skew and stretching distortions. An enhanced Zernike moment descriptor (EZMD) is proposed to overcome this drawback. The EZMD is acquired by deriving ZMD from the rotation and scale normalized shape. The rotation and scale normalization effectively suppress the uneven shape distribution within the unit disk and compensates the skew and stretching. Consequently, more shape information is used in the feature extraction. Experimental results show that the proposed EZMD outperforms ZMD significantly.

Shape description is one of the key parts of image content description in MPEG-7. Most existing shape descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. A generic Fourier descriptor (GFD) is proposed to overcome the drawbacks of existing shape representation techniques. The proposed shape descriptor is derived by applying a 2D Fourier transform on a polar raster sampled shape image. The acquired shape descriptor is application independent and robust. Experimental results show the GFD outperforms the Zernike moment descriptor (ZMD) which has been proposed as the shape descriptor for MPEG-7.

Shape representation is a fundamental issue in the newly emerging multimedia applications. In the content based image retrieval (CBIR), shape is an important low level image feature. Many shape representations have been proposed. However, for CBIR, a shape representation should satisfy several properties such as affine invariance, robustness, compactness, low computation complexity and perceptual similarity measurement. Against these properties, in this paper we attempt to study and compare several shape descriptors which have been widely adopted for CBIR, they are: Fourier descriptors (FD), curvature scale space (CSS) descriptors (CSSD), Zernike moment descriptors (ZMD) and grid descriptors (GD). The strengths and limitations of these methods are analyzed and clarified. Retrieval results are given to show the comparison.