Color Invariant Features Research Articles

Multi-script text versus non-text classification of regions in scene images

Text versus non-text region classification is an essential but difficult step in scene-image analysis due to the considerable shape complexity of text and background patterns. There exists a high probability of confusion between background elements and letter parts. This paper proposes a feature-based classification of image blocks using the color autocorrelation histogram (CAH) and the scale-invariant feature transform (SIFT) algorithm, yielding a combined scale and color-invariant feature suitable for scene-text classification. For the evaluation, features were extracted from different color spaces, applying color-histogram autocorrelation. The color features are adjoined with a SIFT descriptor. Parameter tuning is performed and evaluated. For the classification, a standard nearest-neighbor (1NN) and a support-vector machine (SVM) were compared. The proposed method appears to perform robustly and is especially suitable for Asian scripts such as Kannada and Thai, where urban scene-text fonts are characterized by a high curvature and salient color variations.

Geometrically invariant color, shape and texture features for object recognition using multiple kernel learning classification approach

The geometrically invariant image descriptors are very important for recognizing objects of arbitrary shapes and orientations. We propose a framework for the fusion of the geometrically invariant descriptors representing color, shape, and texture for the recognition of color objects using multiple kernel learning (MKL) approach. To describe texture of color images, we propose an effective rotation invariant texture descriptor which is based on the Zernike moments (ZMs) of the gradient of the color images, referred to as the GZMs. For the shape features, we use the ZMs of the intensity component of a color image and also use multi-channel ZMs (MZMs) which have proven to be superior in performance than the quaternion ZMs (QZMs). For comparative performance analysis, rotation invariants of the QZMs (RQZMs) are also considered. Since the color histograms (CH) are known to be very effective color descriptors, we consider them for representing color. The five sets of features – CH, ZMs, GZMs, MZMs, and RQZMs are invariant to translation, rotation, and scale. The fusion of color, shape and texture features in different combinations using the MKL approach is shown to provide very high recognition rates on PASCAL VOC 2005, Soccer, SIMPLIcity, Flower, and Caltech-101 datasets.

Greedy Learning of Deep Boltzmann Machine (GDBM)’s Variance and Search Algorithm for Efficient Image Retrieval

Despite extensive research on content-based image retrieval, challenges such as low accuracy, incapability to handle complex queries and high time consumption persist. Initially, a preprocessing technique is introduced in this study, a technique that uses a median filter to remove noise to achieve improved accuracy and reliability. Then, Fourier and circularity descriptors are extract in an effective manner correspondent to the texture and affine shape adaptation features. In addition, various descriptors, such as color histogram, color moment, color autocorrelogram and color coherency vector, are extracted as the invariant color features. The multiple ant colony optimization (MACOBTC) approach is implemented with whole features to find relevant features. Finally, the relevant features are utilized for the greedy learning of deep Boltzmann machine classifier (GDBM). The proposed approach obtains effective performance and accurate results on four datasets and is analyzed with various parameters such as accuracy, precision, recall, Jaccard, Dice, and Kappa coefficients. The GDBM provides a 25% increase in accuracy compared with existing techniques, such as the a priori classification algorithm.

Invariant color features–based foreground segmentation for human‐computer interaction

Foreground segmentation is a critical early step in most human‐computer interaction applications notably in action and gesture recognition domain. In this paper, an approach to model background which based on luminance‐invariant color with an adaptive Gaussian mixture is proposed to discriminate foreground object from their background in complex scene. Firstly, the background model is learned based on the spectral properties of shadows and scene activity. Secondly, the shadow with the hypotheses on color invariance is adaptively set up and updated. Finally, the log‐likelihood measurement is to conduct the adaptation. Our experiments are performed on a wide range of practical applications of gesture and action recognition videos. Additionally, the proposed approach is efficient and more robust than premature state‐of‐the‐art with no sacrificing real‐time performance.

Exposing region duplication through local geometrical color invariant features

Many advanced image-processing softwares are available for tampering images. How to determine the authenticity of an image has become an urgent problem. Copy–move is one of the most common image forgery operations. Many methods have been proposed for copy–move forgery detection (CMFD). However, most of these methods are designed for grayscale images without any color information used. They are usually not suitable when the duplicated regions have little structure or have undergone various transforms. We propose a CMFD method using local geometrical color invariant features to detect duplicated regions. The method starts by calculating the color gradient of the inspected image. Then, we directly take the color gradient as the input for scale invariant features transform (SIFT) to extract color-SIFT descriptors. Finally, keypoints are matched and clustered before their geometrical relationship is estimated to expose the duplicated regions. We evaluate the detection performance and computational complexity of the proposed method together with several popular CMFD methods on a public database. Experimental results demonstrate the efficacy of the proposed method in detecting duplicated regions with various transforms and poor structure.

An efficient approach for shadow detection based on Gaussian mixture model

An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate (the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.

A fast compression-based similarity measure with applications to content-based image retrieval

Compression-based similarity measures are effectively employed in applications on diverse data types with a basically parameter-free approach. Nevertheless, there are problems in applying these techniques to medium-to-large datasets which have been seldom addressed. This paper proposes a similarity measure based on compression with dictionaries, the Fast Compression Distance (FCD), which reduces the complexity of these methods, without degradations in performance. On its basis a content-based color image retrieval system is defined, which can be compared to state-of-the-art methods based on invariant color features. Through the FCD a better understanding of compression-based techniques is achieved, by performing experiments on datasets which are larger than the ones analyzed so far in literature.

Color-invariant three-dimensional feature descriptor for color-shift-model-based image processing

We present a novel color-invariant depth feature descriptor for color-shift-model (CSM)-based image processing. Color images acquired by a single camera equipped with multiple color-filter aperture (MCA) contain depth-dependent color misalignment. The amount and direction of the misalignment provides object's distance from the camera. The CSM-based image processing, which represents the combined image-acquisition and depth-estimation framework, requires a color-invariant feature descriptor that can convey depth information. For improving depth-estimation performance, color boosting is performed on a color image acquired by the MCA camera, and CSM-based channel-shifting descriptor vectors, or channel-shifting vectors (CSVs), are generated by using the feasibility test. Color-invariant features are also extracted in the luminance image. The proposed color-invariant three-dimensional (3-D) feature descriptor is finally obtained by combining the CSVs and luminance features. We present experimental analysis of the proposed feature descriptor and show that the descriptors are proportional to the depth of an object. The proposed descriptor can be used for feature-based image matching in various applications, including 3-D scene modeling, 3-D object recognition, 3-D video tracking, and multifocusing, to name a few.

색상 불변 특징을 이용한 고해상도 위성영상의 영역기반 건물 추출

This paper presents a method for region-based building extraction from high resolution satellite images(HRSI) using integrated information of spectral and color invariant features without user intervention such as selecting training data sets. The purpose of this study is also to evaluate the effectiveness of the proposed method by applying to IKONOS and QuickBird images. Firstly, the image is segmented by the MSRG method. The vegetation and shadow regions are automatically detected and masked to facilitate the building extraction. Secondly, the region merging is performed for the masked image, which the integrated information of the spectral and color invariant features is used. Finally, the building regions are extracted using the shape feature for the merged regions. The boundaries of the extracted buildings are simplified using the generalization techniques to improve the completeness of the building extraction. The experimental results showed more than 80% accuracy for two study areas and the visually satisfactory results obtained. In conclusion, the proposed method has shown great potential for the building extraction from HRSI.

Panoramic video using scale-invariant feature transform with embedded color-invariant values

The resolutions offered by today's multimedia vary significantly owing to the development of video technology. For example, there is a huge gap between the resolution of cellular phones as small input devices and beam projectors as large output devices. Thus, panoramic video technology is one method that can convert a small resolution into a large resolution to lend realism and wide vision to a scene. Yet, transforming the resolution of an image requires feature or object matching based on extracting important information from the image, where the scale-invariant feature transform (SIFT) is one of the most robust and widely used methods. However, identifying corresponding points becomes difficult in the case of changing illumination or two surfaces with a similar intensity, as SIFT extracts features using only gray information. Therefore, this paper proposes a method of image stitching based on color-invariant features for automated panoramic videos. Color-invariant features can discount the illumination, highlights, and shadows in a scene, as they include the property of the surface reflectance independent of illumination changes. The effectiveness and accuracy of the feature matching with the proposed algorithm are verified using objects and illuminations in a booth, followed by panoramic videos.

Content-based image retrieval using visually significant point features

This paper presents a new image retrieval scheme using visually significant point features. The clusters of points around significant curvature regions (high, medium, and weak type) are extracted using a fuzzy set theoretic approach. Some invariant color features are computed from these points to evaluate the similarity between images. A set of relevant and non-redundant features is selected using the mutual information based minimum redundancy-maximum relevance framework. The relative importance of each feature is evaluated using a fuzzy entropy based measure, which is computed from the sets of retrieved images marked relevant and irrelevant by the users. The performance of the system is evaluated using different sets of examples from a general purpose image database. The robustness of the system is also shown when the images undergo different transformations.

Material-specific adaptation of color invariant features

For the modeling of materials, the mapping of image features onto a codebook of feature representatives receives extensive treatment. For reason of their generality and simplicity, filterbank outputs are commonly used as features. The MR8 filterbank of Varma and Zisserman is performing well in a recent evaluation. In this paper, we construct color invariant filter sets from the original MR8 filterbank. We evaluate several color invariant alternatives over more than 250 real-world materials recorded under a variety of imaging conditions including clutter. Our contribution is a material recognition framework that learns automatically for each material specifically the most discriminative filterbank combination and corresponding degree of color invariance. For a large set of materials each with different physical properties, we demonstrate the material-specific filterbank models to be preferred over models with fixed filterbanks.

Color invariant object recognition using entropic graphs

AbstractWe present an object recognition approach using higher‐order color invariant features with an entropy‐based similarity measure. Entropic graphs offer an unparameterized alternative to common entropy estimation techniques, such as a histogram or assuming a probability distribution. An entropic graph estimates entropy from a spanning graph structure of sample data. We extract color invariant features from object images invariant to illumination changes in intensity, viewpoint, and shading. The Henze–Penrose similarity measure is used to estimate the similarity of two images. Our method is evaluated on the ALOI collection, a large collection of object images. This object image collection consists of 1000 objects recorded under various imaging circumstances. The proposed method is shown to be effective under a wide variety of imaging conditions. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 146–153, 2006

Fast occluded object tracking by a robust appearance filter

We propose a new method for object tracking in image sequences using template matching. To update the template, appearance features are smoothed temporally by robust Kalman filters, one to each pixel. The resistance of the resulting template to partial occlusions enables the accurate detection and handling of more severe occlusions. Abrupt changes of lighting conditions can also be handled, especially when photometric invariant color features are used. The method has only a few parameters and is computationally fast enough to track objects in real time.

FOCUS: A system for searching for multi-colored objects in a diverse image database

We describe a new multi-phase, color-based image retrieval system (FOCUS) which is capable of identifying multi-colored query objects in an image in the presence of significant, interfering backgrounds. The query object may occur in arbitrary sizes, orientations, and locations in the database images. Scale and rotation invariant color features have been developed to describe an image, such that the matching process is fast even in the case of complex images. The first phase of processing matches the query object color with the color content of an image computed as the peaks in the color histogram of the image. The second phase matches the spatial relationships between color regions in the image with the query using a spatial proximity graph (SPG) structure designed for the purpose. Processing at coarse granularity is preferred over pixel-level processing to produce simpler graphs, which significantly reduces computation time during matching. The speed of the system and the small storage overhead make it suitable for use in large databases with online user interfaces. Test results with multi-colored query objects from man-made and natural domains show that FOCUS is quite effective in handling interfering backgrounds and large variations in scale. The experimental results on a database of diverse images highlights the capabilities of the system.

Color invariance

This paper presents the measurement of colored object reflectance, under different, general assumptions regarding the imaging conditions. We exploit the Gaussian scale-space paradigm for color images to define a framework for the robust measurement of object reflectance from color images. Object reflectance is derived from a physical reflectance model based on the Kubelka-Munk theory for colorant layers. Illumination and geometrical invariant properties are derived from the reflectance model. Invariance and discriminative power of the color invariants is experimentally investigated, showing the invariants to be successful in discounting shadow, illumination, highlights, and noise. Extensive experiments show the different invariants to be highly discriminative, while maintaining invariance properties. The presented framework for color measurement is well-founded in the physics of color as well as in measurement science. Hence, the proposed invariants are considered more adequate for the measurement of invariant color features than existing methods.