A multi-view-group non-negative matrix factorization approach for automatic image annotation

Multimedia Tools and Applications - In automatic image annotation (AIA) different features describe images from different aspects or views. Part of information embedded in some views is common for...     

Using contourlet transform and cover selection for secure steganography

In this paper, we present a new adaptive contourlet-based steganography method that hides secret data in a specific or automatically selected cover image. Our proposed steganography method primarily decomposes the cover image by contourlet transform. Then, every bit of secret data is embedded by increasing or decreasing the value of one coefficient in a block of a contourlet subband. Contourlet coefficients are manipulated relative to their magnitudes to hide the secret data adaptively. In addition to proposing contourlet-based steganography method, this work investigates the effect of cover selection on steganography embedding and steganalysis results. We demonstrate, through the experiments, that choosing suitable cover image by a proper selection measure could help the steganographer reduce detectability of stego images. The proposed technique is examined with some state-of-the-art steganalysis methods, and the results illustrate that an image can successfully hide secret data with average embedding capacity of 0.02 bits per pixel in a random selected cover image. Cover selection improves the embedding capacity up to 0.06 bits per pixel. Several experiments and comparative studies are performed to show the effectiveness of the proposed technique in enhancing the security of stego images, as well as to demonstrate its gain over the previous approaches in literature.     

Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes

The fish freshness was evaluated using machine vision technique through color changes of eyes and gills of farmed and wild gilthead sea bream (Sparus aurata), being employed lightness (L^*), redness (a^*), yellowness (b^*), chroma (c^*), and total color difference (ΔE) parameters during fish ice storage. A digital color imaging system, calibrated to provide accurate CIELAB color measurements, was employed to record the visual characteristics of eyes and gills. The region of interest was automatically selected using a computer program developed in MATLAB software. L^*, b^*, and ΔE of eyes increased with storage time, while c^* decreased. The a^* parameter of fish eyes did not show clear a trend with storage time. The L^*, b^*, and ΔE of fish gills increased with storage time, but a^* and c^* decreased. Regression analysis and artificial neural networks approaches were used to correlate the eyes and gills color parameters with the time of storage and a strong correlation was found between color parameters and storage day. Gills color changes were more precise than those found for eyes in order to evaluate the fish freshness. However, the gills cover should be removed for taking the images and thus, the method is destructive and time-consuming. Therefore, the color parameters of fish eyes can be used as a green, low cost and easy method for fast and on-line assessing of fish freshness in food industry.     

The effect of distillation methods and stage of plant growth on the essential oil content and composition of Satureja rechingeri Jamzad

The aerial parts of Satureja rechingeri were collected in two stages of plant growth (at the beginning of and full flowering stage) from Ilam province in the west of Iran. The essential oils were isolated by steam, hydro- and water-steam-distillation from the aerial parts at complete flowering stage. In addition, the essential oil of plant material at the beginning of flowering was obtained by the hydro-distillation. The oils were analyzed by capillary GC and GC–MS. The highest oil yield was obtained by hydro-distillation method and the lowest by steam-distillation. The highest oil yield was obtained at the beginning of flowering (4.72% w/w). The oil yields at full flowering stage were 2.46–4.24% (the highest for hydro-distillation and the lowest for steam-distillation).     

CBS: Contourlet-Based Steganalysis Method

An ideal steganographic technique embeds secret information into a carrier cover object with virtually imperceptible modification of the cover object. Steganalysis is a technique to discover the presence of hidden embedded information in a given object. Each steganalysis method is composed of feature extraction and feature classification components. Using features that are more sensitive to information hiding yields higher success in steganalysis. So far, several steganalysis methods have been presented which extract some features from DCT or wavelet coefficients of images. Multi-scale and time-frequency localization of an image is offered by wavelets. However, wavelets are not effective in representing the images in different directions. Contourlet transform addresses this problem by providing two additional properties, directionality and anisotropy. The present paper offers an universal approach to steganalysis called CBS, which uses statistical moments of contourlet coefficients as features for analysis. After feature extraction, a non-linear SVM classifier is applied to classify cover and stego images. The efficiency of the proposed method is demonstrated by experimental investigations. The proposed steganalysis method is compared with two well-known steganalyzers against typical steganography methods. The results showed the superior performance of our method.     

Secure steganography based on embedding capacity

Mostly the embedding capacity of steganography methods is assessed in non-zero DCT coefficients. Due to unequal distribution of non-zero DCT coefficients in images with different contents, images with the same number of non-zero DCT coefficients may have different actual embedding capacities. This paper introduces embedding capacity as a property of images in the presence of multiple steganalyzers, and discusses a method for computing embedding capacity of cover images. Using the capacity constraint, embedding can be done more secure than the state when the embedder does not know how much data can be hidden securely in an image. In our proposed approach, an ensemble system that uses different steganalyzer units determines the security limits for embedding in cover images. In this system, each steganalyzer unit is formed by a combination of multiple steganalyzers from the same type, which are sensitive to different payloads. The confidence of each steganalyzer on an image leads us to determine the upper bound of embedding rate for the image. Considering embedding capacity, the steganographer can minimize the risk of detection by selecting a proper cover image that is secure for a certain payload. Moreover, we analyzed the relation between complexity and embedding capacity of images. Experimental results showed the effectiveness of the proposed approach in enhancing the security of stego images.     

Motion blur identification in noisy images using mathematical models and statistical measures

Motion blur is one of the most common blurs that degrades images. Restoration of such images is highly dependent on estimation of motion blur parameters. Since 1976, many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and robustness in noisy environments. In this paper, we have presented a novel algorithm to estimate linear motion blur parameters such as direction and length. We used Radon transform to find direction and bispectrum modeling to find the length of motion. Our algorithm is based on the combination of spatial and frequency domain analysis. The great benefit of our algorithm is its robustness and precision in noisy images. We used statistical measures to prove goodness of our model. Our method was tested on 80 standard images that were degraded with different directions and motion lengths, with additive Gaussian noise. The error tolerance average of the estimated parameters was 0.9° in direction and 0.95 pixel in length and the standard deviations were 0.69 and 0.85, respectively.