Content based retrieval for remotely sensed imagery

Texture feature has been widely used in object recognition, image content analysis and many others. Among various approaches to texture feature extraction, Gabor filter has emerged as one of the most popular ones. Gabor filter-based feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smooth parameters of Gaussian envelope. In the literature, different parameter settings have been suggested, and filter banks created by these parameter settings work well in general. From the perspective of pattern classification, however, filter banks thus designed may not be ideal. In the present study, we propose a new approach to Gabor filter bank design, by incorporating feature selection, i.e. filter selection, into the design process. The merits of incorporating filter selection in filter bank design are twofold. Firstly, filter selection produces a compact Gabor filter bank and hence reduces computational complexity of texture feature extraction. Secondly, Gabor filter bank thus designed produces low-dimensional feature representation with improved sample-to-feature ratio, and this in turn leads to improved performance of texture classification. Experiment results on benchmark datasets and a real application have demonstrated the effectiveness of the proposed method.

PolSAR image segmentation based on feature extraction and data compression using Weighted Neighborhood Filter Bank and Hidden Markov random field-expectation maximization

This paper presents a novel style of Gabor filter banks designed for plant species recognition using their bark texture features. In this paper, texture is modeled as multiple narrowband signals that are characterized by their central frequencies and normalized ratios of amplitudes. The normalized ratio of amplitudes is employed as an energy weight for combining narrowband signals. Based on this texture model, a set of texture features can be extracted from each kind of plant bark that is used to characterize the plant and to design the corresponding Gabor filter bank. A classifier is constructed by these Gabor filter banks. Plant recognition experiments on a small database of bark images have been conducted and the effectiveness of our approach is confirmed by the experimental results.

<p>Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT), and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed. </p>

In this research we concentrate on adaptive texture feature extractors which are automatically extracted from an image collection. We use independent component analysis (ICA) to extract independent component filters (ICF). ICF have previously been shown to include edge filters, having properties similar to the receptive fields of simple cells of the human visual cortex. In this thesis we evaluate the utility of ICF-based collection-specific features in the context of content-based image retrieval (CBIR), with the view to demonstrate the viability of using such automatic collection-specific features. We find that global features extracted using a small number of ICF outperform those extracted by a bank of Gabor filters, even when very large number of Gabors are used. We also present comparisons against a variety of state-of-the-art features and show that ICF-based features perform better than these, without the need for any hand-tuning. ICA extracts large number of filters. In order to find a useful smaller subset we evaluate a previously-published variance-based filter selection method. We identify the shortcomings of this method. Our proposed improvements to filter-based feature selection and extraction, response scaling and locally normalised convolution, was a result of trying to address some of these shortcomings. Even with these improvements the variance-based method has an intrinsic flaw of being susceptible to selecting redundant filters. We propose a new filter selection method based on normalised cross-correlation and clustering. We show that our method selects a more useful subset compared to the previously published variance-based method. We also propose a salient-point based local feature which uses ICF and show that our proposed feature performs better than SIFT features and also the global ICF features. We further illustrate the utility of ICFSIFT by developing a grid-based adaptive local feature and we demonstrate that ICFSIFT extracts different and useful information compared to the ICF-based global and grid-based local features. After applying the techniques proposed in this thesis to four standard texture collections, we show that, while hand-tuned features can work well for their target collection, the automatic adaptive features perform better for the general case.

This study represents an innovative automatic method for black and white films colorization using texture features and a multilayer perceptron artificial neural network. In the proposed method, efforts are made to remove human interference in the process of colorization and replace it with an artificial neural network (ANN) which is trained using the features of the reference frame. Later, this network is employed for automatic colorization of the remained black and white frames. The reference frames of the black and white film are manually colored. Using a Gabor filter bank, texture features of all the pixels of the reference frame are extracted and used as the input feature vector of the ANN, while the output will be the color vector of the corresponding pixel. Finally, the next frames’ feature vectors are fed respectively to the trained neural network, and color vectors of those frames are the output. Applying AVI videos and using various color spaces, a series of experiments are conducted to evaluate the proposed colorization process. This method needs considerable time to provide a reasonable output, given rapidly changing scenes. Fortunately however, due to the high correlation between consecutive frames in typical video footage, the overall performance is promising regarding both visual appearance and the calculated MSE error. Apart from the application, we also aim to show the importance of the low level features in a mainly high level process, and the mapping ability of a neural network.

In this chapter, a content-based image retrieval technique based on the concept of region-based image retrieval has been described. This technique integrates color, texture, and shape features using local binary patterns (LBPs). In this technique, the image is divided into a fixed number of blocks and from each block LBP-based color, texture, and shape features are computed. The color and texture features are extracted using LBP histograms of quantized color image and gray-level images, respectively. Shape features are computed using the binary edge map obtained using Sobel edge detector from each block. All three features are combined to make a single completed binary region descriptor (CBRD) represented in the LBP way. To support region-based retrieval, an effective region code-based scheme is employed. In this technique, the spatial relative locations of objects are also considered to increase the retrieval accuracy.

Batik is a traditional craft of designing patterned fabrics which hold high artistic value in Sri Lankan culture, where hand-painted wax patterns are coloured using specialist dyeing methods to create the finished product. This paper presents a study of vision-based feature extraction of Batik images considering colour, texture and shape features to develop a comprehensive feature descriptor of Batik motifs. Wax drawn patterns are identified from the digital images of Batik motifs to retrieve an outline of patterns demarcating the different coloured layers generated by multiple stages of dyeing. Motifs with repetitive patterns are identified using the Local Binary Pattern (LBP) as a texture feature vector. Both RGB and L*a*b* colour schemes are studied in the representation of Batik motifs. The colour description is presented using Mini Batch K-Means which out-performed the widely used K-Means clustering method. Hu Moment Invariants are used for shape feature extraction, and Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. A comprehensive feature descriptor is developed to represent Batik designs, which could be used to recommend similar designs based on the shape and texture features of query images presented by the user.

With the rapid development of 3S technologies, content-based retrieval from remote sensing images provides an important means of information acquisition and sharing and has become a key technology in digital earth construction. This paper puts the emphasis on using texture features for retrieval of aerial image database. M-band wavelet theory is introduced and M-band wavelet histogram technology is applied to texture extraction. Some experimental results are given to evaluate the retrieval performance of our method and to compare with that of method based on traditional two-band wavelet transform, followed by conclusions

In everyday life whenever people observe, interact or speak to each other, visual attention is mostly directed toward the other person’s face, particularly to the eyes and the nearby periocular regions. This is naturally reflected when the user interacts with their mobile phones in several usual activities, such as web access, payments and video calls. For this reason, the functionality of mobile devices is strongly affected by the design of the user interface. In this chapter, we propose a biologically inspired approach for continuous user authentication based on the analysis of the ocular regions. The proposed system is based on a modified version of the HMAX visual processing module. HMAX is a hierarchical model which has been conceived to mimic the basic neural architecture of the ventral stream of the visual cortex. The original HMAX model consists of four layers: S1, C1, S2 and C2. S1 and C1 represent the responses to a bank of orientation-selective Gabor filters. S2 and C2 represent the responses of simple and complex cells to other textural features. The discrimination power of HMAX in recognizing classes of objects is invariant to rotation and scale. The C1 layer, which is mainly responsible for the scale and rotation invariance, is implemented using a max-pooling operation, which may lose some spatial information. To overcome this problem while preserving the maximal visual acuity and hence the localization accuracy, we propose to augment the model by applying a retinal log-polar mapping. The log-polar mapping is an approximation of the retino-cortical mapping that is performed by the early stages of the primate visual system. Due to the high density of the cones in the fovea, the log-polar approximation of the space-variant distribution model of the photoreceptors can only be applied outside the foveal region. Therefore, the log-polar mapping is added to the HMAX model as a complementary stage to process the peripheral region of the grabbed images. In order to demonstrate the feasibility of the proposed approach to mobile scenarios, experimental results obtained from publicly available databases and image streams grabbed from mobile devices will be presented.

Vertebral fractures are important indicators of osteoporosis. Fractures with partial collapse of vertebral bodies are referred to as vertebral compression fractures (VCFs) that are usually non-traumatic in nature. Some common causes of VCFs are trauma, bone failure related to osteoporosis (benign) and metastatic cancer (malignant). This paper aims at developing a system for computer-aided diagnosis to help in the detection, labeling and segmentation of lumbar vertebral body (VB) and to further classify each VB into normal, malignant and benign VCFs. After the initial preprocessing, morphological, shape and angular features are used in the detection, labeling and segmentation steps. Various shape and statistical texture features are extracted from the segmented VB and are fed to the classifier for the final decision. The segmentation and classification results obtained were compared with the ground truth manual segmentation of the lumbar VB and the decision labels of the fractures provided by the experts. The dice similarity coefficient (DSC) for segmentation reached up to 94.27%, and the classification results show that shape and texture features together are able to correctly classify with an accuracy rate of 95.34%. The final outcomes are expected to be useful in the analysis of vertebral compression fractures.

BackgroundSpine magnetic resonance image (MRI) plays a very important role in the diagnosis of various spinal diseases, such as disc degeneration, scoliosis, and osteoporosis. Accurate localization and segmentation of the intervertebral disc (IVD) in spine MRI can help accelerate the diagnosis time and assist in the treatment by providing quantitative parameters. In this paper, a method based on Gabor filter bank is proposed for IVD localization and segmentation.MethodsFirst, the structural features of IVDs are extracted using a Gabor filter bank. Second, the Gabor features of spine are calculated and spinal curves are detected. Third, the Gabor feature images (GFI) of IVDs are calculated and adjusted according to the spinal curves. Fourth, the IVDs are localized by clustering analysis with GFI. Finally, an optimum grayscale-based algorithm with self-adaptive threshold, combined with the localization results and Gabor features of the spine, is performed for IVDs segmentation.ResultsThe proposed method is verified by an MRI dataset consisting of 278 IVDs from 37 patients. The accuracy of localization is 98.23 % and the dice similarity index for segmentation evaluation is 0.9237.ConclusionsThe proposed Gabor filter based method is effective for IVD localization and segmentation. It would be useful in computer-aided diagnosis of IVD diseases and computer-assisted spine surgery.

In this paper, we propose an approach for representing both shape and texture information in an image using a single hybrid feature descriptor for Content Based Image Retrieval. Towards this, we compute the gradient magnitude of the input image prior to deriving features. Feature extraction is then performed using the responses from a bank of Gabor filters. Here, we exploit the fact that shape corresponds to the high spatial frequency content in the image whereas natural texture information predominantly lies within low to mid-range frequencies. This approach helps in better localization of characteristic texture as well as shape, due to spread of energy towards high frequencies in spectral domain. Moment invariants are extracted from Gabor filter responses which yield better retrieval performance than conventional statistical features. Experimental results show that this approach has relatively improved retrieval performance on Corel image data set when compared with recent approaches in the literature. Further experiments were also performed on a medical image dataset with 95.4 percent precision and 74.6 percent recall.

This paper proposes a method for texture feature extraction by integrating Gabor filters and independent component analysis (ICA) for Uyghur handwriting based writer identification. That is, the texture image is firstly filtered by a given bank of Gabor filters, and then higher dimensional feature vectors are constructed from the filtered texture images. Next, the dimensionality of these vectors is reduced by means of principal component analysis (PCA). Finally, the independent components in the resulting vectors with dimensionality reduced are analyzed and extracted by us. Experiments were performed using KNN-5 classifier to Uyghur handwriting samples from 55 different people and promising results of 92.5% correct identification rate were achieved.

Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans