Perceptual Colour Features for Natural Scene Image Description and Retrieval

While extensive references are present in the literature dealing with the correlation between subjective and objective evaluation of tooth shade, there is a lack of information on this correlation regarding the soft tissue color. The purpose of this experimental study was to verify whether a correlation between the objective and subjective evaluation exists in analyzing soft tissue color. A total of 39 patients with at least one implant-supported restoration in the anterior maxilla were included in the study. The shade of the peri-implant mucosa was compared with the shade of the gingiva at the adjacent tooth in a subjective and in an objective manner. The subjective evaluation was performed by five dental professionals (prosthodontist, periodontist, general dentist, dental hygienist, and dental assistant) in a subjective scale (ranging from 1 to 4). The objective evaluation was obtained by means of a spectrophotometer in a CIELAB* Color Scale, and the differences were evaluated through formula ΔE=[(ΔL)²+(Δa)²+(Δb)²]¹/². To correlate the subjective and the objective evaluation, for each arithmetical median value of the subjective evaluation, a mean value of objective evaluation has been calculated, and the Spearman's rank correlation coefficient has been used. The differences have been also analyzed for thin and thick tissue biotypes. The mean ∆E value for the subjective evaluation between peri-implant soft tissue and adjacent tooth gingival tissue was ∆E = 9.74. Also, mean ∆E values of 10.35 and 7.54 have been reported for thin and thick biotypes, respectively. Mean values of ∆E = 6.63, 8.54, and 15.54 were presented by median values of 1 (perfect matching), 2 (good matching), and 3 (clinically distinguishable), respectively. The threshold for the distinction of differences of mucosal color by the human eyes between perfect or good matching and distinguishable values has been calculated in ∆E = 8.74. Within the limitation of this study, a correlation between the subjective and the objective evaluation of the peri-implant soft tissue exists and the threshold for the distinction of mucosal color differences between perfect or good matching and distinguishable subjective values has been calculated in ∆E = 8.74 in the objective evaluation.

For the limited transmission capacity and compressed images in the network environment, a compressed-domain image filtering and re-ranking approach for multi-agent image retrieval is proposed in this paper. Firstly, the distributed image retrieval platform with multi-agent is constructed by using Aglet development system, the lifecycle and the migration mechanism of agent is designed and planned for multi-agent image retrieval by using the characteristics of mobile agent. Then, considering the redundant image brought by distributed multi-agent retrieval, the duplicate images in distributed retrieval results are filtered based on the perceptual hashing feature extracted in the compressed-domain. Finally, weight-based hamming distance is utilized to re-rank the retrieval results. The experimental results show that the proposed approach can effectively filter the duplicate images in distributed image retrieval results as well as improve the accuracy and speed of compressed-domain image retrieval.

Self organizing natural scene image retrieval

Simple SummaryOral cancer has a high mortality rate. Then, oral cancer screening is needed for early detection and treatment. Fluorescence visualization is non-invasive, convenient, and in real-time, and examinations can be repeated. Our study aimed to show the usefulness of oral cancer screening with fluorescence visualization. A total of 502 patients were performed using fluorescence visualization that was analyzed using subjective and objective evaluation. Results of this study, subjective evaluation for detection oral cancer was high sensitivity and low specificity, while objective evaluation using imaging processing analysis was high sensitivity and high specificity. Therefore, oral cancer screening using fluorescence visualization is useful for the detection of oral cancer. The widespread use of this screening can reduce the mortality rate of oral cancer.Background: Oral cancer screening is important for early detection and early treatment, which help improve survival rates. Biopsy is the gold standard for a definitive diagnosis but is invasive and painful, while fluorescence visualization is non-invasive, convenient, and real-time, and examinations can be repeated using optical instruments. The purpose of this study was to clarify the usefulness of fluorescence visualization in oral cancer screening. Methods: A total of 502 patients, who were examined using fluorescence visualization with optical instruments in our hospitals between 2014 and 2019, were enrolled in this study. The final diagnosis was performed by pathological examination. Fluorescence visualization was analyzed using subjective and objective evaluations. Results: Subjective evaluations for detecting oral cancer offered 96.8% sensitivity and 48.4% specificity. Regarding the objective evaluations, sensitivity and specificity were 43.7% and 84.6% for mean green value, 55.2% and 67.0% for median green value, 82.0% and 44.2% for coefficient of variation of value, 59.6% and 45.3% for skewness, and 85.1% and 75.8% for value ratio. For the sub-analysis of oral cancer, all factors on objective and subjective evaluation showed no significant difference. Conclusions: Fluorescence visualization with subjective and objective evaluation is useful for oral cancer screening.

The gear whine sound of an axle system is one of the most important sound qualities in a sports utility vehicle (SUV). Previous work has shown that, because of masking effects, it is difficult to evaluate the gear whine sound objectively by using only the A-weighted sound pressure level. In this paper, the characteristics of the axle-gear whine sound were first investigated on the basis of synthetic sound technology, and a new objective evaluation method for this sound was developed by using sound metrics, which are the psychoacoustic parameters, and the artificial neural network (ANN) used for the modelling of the correlation between objective and subjective evaluation. This model developed by using ANN was applied to the objective evaluation of the axle-gear whine sound for real SUVs and the output of the model was compared with subjective evaluation. The results indicate a good correlation of over 90 per cent between the subjective and objective evaluations.

The notion of perceptual features is introduced for describing general music properties based on human perception. This is an attempt at rethinking the concept of features, aiming to approach the underlying human perception mechanisms. Instead of using concepts from music theory such as tones, pitches, and chords, a set of nine features describing overall properties of the music was selected. They were chosen from qualitative measures used in psychology studies and motivated from an ecological approach. The perceptual features were rated in two listening experiments using two different data sets. They were modeled both from symbolic and audio data using different sets of computational features. Ratings of emotional expression were predicted using the perceptual features. The results indicate that (1) at least some of the perceptual features are reliable estimates; (2) emotion ratings could be predicted by a small combination of perceptual features with an explained variance from 75% to 93% for the emotional dimensions activity and valence; (3) the perceptual features could only to a limited extent be modeled using existing audio features. Results clearly indicated that a small number of dedicated features were superior to a "brute force" model using a large number of general audio features.

A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. To address these concerns, this paper presents a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. Such computing methodology is fairly desirable for image retrieval over large image databases.

A perception-based approach to content-based image representation and retrieval is proposed in this paper. We consider textured images and propose to model their textural content by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast, and busyness. The proposed computational measures can be based upon two representations: the original images representation and the autocorrelation function (associated with original images) representation. The set of computational measures proposed is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results and benchmarking show interesting performance of our approach. First, the correspondence of the proposed computational measures to human judgments is shown using a psychometric method based upon the Spearman rank-correlation coefficient. Second, the application of the proposed computational measures in texture retrieval shows interesting results, especially when using results fusion returned by each of the two representations. Comparison is also given with related works and show excellent performance of our approach compared to related approaches on both sides: correspondence of the proposed computational measures with human judgments as well as the retrieval effectiveness.

Correlation analysis of subjective and objective evaluation for automobile sound quality is an important topic in automobile technology fields. Considering the theoretical characteristics of grey system method and neural network theory, grey relational analysis was applied to analyse the contributions of the variables of objective evaluation to subjective evaluation using the existing subjective and objective evaluation data of automobile horn sound quality. Then the main objective variables affecting the subjective feeling were determined, which include sound pressure level, A-weighted sound pressure level and sharpness. Back-propagation neural network model was designed and established as a quantitative expression between subjective and objective evaluation data. The simulation results of neural network model accorded with the original value of the subjective evaluation data very well, which illustrated the reliability of this method.

Signs are ubiquitous indoors and outdoors, which are used for way finding, finding shops and businesses, accessing variety of services. But the information of signs is inaccessible to many visually impaired people unless they are represented in a non-visual form such as Braille, tactile graphic, and speech. Automatic reading text from signs in natural images becomes a vital application in visually impaired people assistance. However, finding the text in scene images is a great challenge, because it cannot be assumed that the acquired image contains only characters. Natural scene images usually contain diverse complex text of different size, styles and colors with complex backgrounds. Therefore, this paper proposes a novel method for text extraction from scene images. The algorithm is implemented and evaluated using a set of natural scene images. Accuracy, precision and recall rates of the proposed method are analyzed to determine the success and limitation. Recommendations for improvements are given based on the results.

Driving comfort, which is mainly influenced by vibration and shock, is an essential factor to evaluate the performance of intelligent vehicles. The evaluation methods of driving comfort mainly contain subjective and objective evaluation. Subjective evaluation is time-consuming, expensive and sensitive to personal feelings. And objective evaluation is difficult to exactly define the relationship between objective parameters and driving comfort. In order to combine the advantages of subjective and objective evaluation, a neural network that adopt objective indicators as input and subjective ratings as output was established for evaluating driving comfort. First, a road test with about 9000 km was conducted and key parameters of vehicle status were recorded, as well as subjective ratings. Secondly, 25,165 segments were extracted from the naturalistic driving data. Then, total weighted root-mean-square accelerations of all segments were computed according to ISO 2631–1997 Standard. And the result shows that the comfort levels calculated by weighted root-mean-square accelerations cannot match the subjective ratings given by professional evaluators very well. Finally, a 20-128-256-256-128-6 BP neural network was established and trained. And the accuracy of evaluation based on neural network is better than evaluation based on weighted root-mean-square value. The result reveals that it is feasible to establish a neural network model based on collected naturalistic driving data to evaluate the driving comfort of vehicles.

Nasal obstruction is characterized by insufficient airflow and increased resistance in the nasal cavities, affecting patients’ quality of life and sleep. This study aims to compare the efficacy of medical and surgical treatment in patients with Nasal obstruction, using subjective evaluations (NOSE and VAS scales) and objective evaluations (active anterior rhinomanometry). This is an experimental and prospective longitudinal study conducted at the Hospital de Clínicas, Paraguay. Forty patients with Nasal obstruction were included, divided into medical treatment (nasal corticosteroids) and surgical treatment (turbinoplasty, septoplasty, or both) groups. Pre- and post-treatment evaluations were performed using the NOSE and VAS scales, and active anterior rhinomanometry. The sample included 52.5% men and 47.5% women, with an average age of 32.18±11.59 years. The main cause of Nasal obstruction was chronic rhinitis (62.5%). Treatments showed significant improvements in subjective and objective evaluations. In the medical group, VAS improved by 2.63 points in women and 2.00 in men, while the surgical group showed more pronounced improvements. The NOSE also showed significant reductions, especially in patients undergoing combined turbinoplasty and septoplasty. Active anterior rhinomanometry reflected an increase in overall nasal flow in all treated groups. Both medical and surgical treatments are effective in improving Nasal obstruction, with greater benefits observed in surgical treatments. Subjective and objective evaluations are valuable and complementary tools for assessing treatment in patients with Nasal obstruction. These results highlight the importance of a comprehensive evaluation in the management of Nasal obstruction.

Studies have been actively undertaken on image retrieval from a large-capacity image database, particularly on image retrievals such as natural image retrieval, character image retrieval or trademark retrieval from a streaming image database. In retrieving a character image, either color or shape information is used for the key feature information. However, changes in the shape of a character image often makes it difficult to retrieve solely based on the shape information, requiring a new retrieval method where both color and shape information are taken into consideration. We present a highly effective method for retrieving or matching similar character images even when shape information differs substantially. In our approach, combined features of color and shape information are used for image retrieval; an image is first split into sectors; color information is extracted from the sector images followed by quantization using Parzen window to extract features; an image is then retrieved by means of bipartite matching using the features. Our results show the retrieval rate using the combined information increases substantially for matching natural or character images compared with the results obtained by the combination of two features independently.

Real and imaginary modulation spectral subtraction for speech enhancement

Deep learning for image retrieval has been used in this era, but image retrieval with the highest accuracy is the biggest challenge, which still lacks auto-correlation for feature extraction and description. In this paper, a novel deep learning technique for achieving highly accurate results for image retrieval is proposed, which implements a convolutional neural network with auto-correlation, gradient computation, scaling, filter, and localization coupled with state-of-the-art content-based image retrieval methods. For this purpose, novel image features are fused with signatures produced by the VGG-16. In the initial step, images from rectangular neighboring key points are auto-correlated. The image smoothing is achieved by computing intensities according to the local gradient. The result of Gaussian approximation with the lowest scale and suppression is adjusted by the by-box filter with the standard deviation adjusted to the lowest scale. The parameterized images are smoothed at different scales at various levels to achieve high accuracy. The principal component analysis has been used to reduce feature vectors and combine them with the VGG features. These features are integrated with the spatial color coordinates to represent color channels. This experimentation has been performed on Cifar-100, Cifar-10, Tropical fruits, 17 Flowers, Oxford, and Corel-1000 datasets. This study has achieved an extraordinary result for the Cifar-10 and Cifar-100 datasets. Similarly, the results of the study have shown efficient results for texture datasets of 17 Flowers and Tropical fruits. Moreover, when compared to state-of-the-art approaches, this research produced outstanding results for the Corel-1000 dataset.