Modulating a Local Shape Descriptor through Biologically Inspired Color Feature

In this work, we employ multi-layer feed forward network algorithm for leaf recognition using its shape, color and vein features. The leaf images are pre-processed and segmented. The shape features like area, convex area, diameter, length, width, perimeter, eccentricity, solidity, major axis length and Minor axis length are extracted by taking the features from the segmented leaf image. The mean, standard deviation, skewness and kurtosis for red, green and blue color features are extracted. Wiener filtering and canny edge detection are used to identify the vein features v1, v2, v3, v4 at different thresholds by calculating gray level value from the gray level histogram. The features are then exposed to Principal Component Analysis (PCA) for feature reduction and the resultant reduced feature set contains five shape features, six color features and three vein features. The features are then taken as input vectors for multi-layer neural network. Total 150 leaves from Columbia leaf image database are taken as samples. 150 leaves represent 10 different leaf species. The system was trained with 104 leaf images and was tested and validated using 23 leaf images each. The success rate of reduced feature set of leaf shapes and color was 84.7% and by combining leaf shape, color and vein features outputted a success rate of 95.3%.

With the emerge of multimedia, communication and processing, a typical image database has a large volume of data. It is an essential to build an efficient retrieval system to browse through the entire database. In this paper we proposed precise Relevance Feed Back (RFB) Content Based Image Retrieval (CBIR) using multiple features based on interactive retrieval approach which will extensively reduces the semantic gap between low-level features and high-level semantics. Relevance Feed Back improves the retrieval accuracy of Content Based Image Retrieval(CBIR) by modifying the query based on the user's feedback in which the user can select the most relevant images and provide a weight of preference for each relevant image. In our approach we extracted multiple features by integrating color, texture and shape features with Relevance Feed Back. We extracted color features by cumulative histogram, texture feature are exploited by using Color co-occurrence matrix (CCM) and we describe the shape information contained in an image on the basis of its significant edges. We used Euclidean distance as the similarity measure. In our experiment we used Corel real-world image databases with 1000 images, divided into 10 categories, each category containing 100 images including landscapes, animals, plants, monuments, transport (cars, planes). Our results reflects that integrating color, texture and shape for retrieving similar images improves the retrieval performance with respect to retrieval accuracy and recall rate as compared with the classical color feature. During the experiment we also observed that incorporating Relevance Feed Back (RFB) with multiple features outperforms in comparison with multifeature vector without Relevance Feed Back method.

Pineapple is one of the potential commodities in Indonesia. This is due to high market demand, potential suitable land in Indonesia and public awareness of fruit supply. The main factor of crop failure in pineapple plants is the delay in handling pineapple plant diseases. To identify in image processing requires class grouping. Self-Organizing Map (SOM) which divides the input pattern into several groups so that the network output is in the form of the group that is most similar to the given input. However, the SOM algorithm requires data input that characterizes an object to facilitate the identification process. So, in this study the SOM algorithm was improved through color feature extraction with parameters Red, Green, Blue, Hue, Saturation and Value, as well as texture feature extraction with parameters contrast, correlation, energy, and homogeneity in the Gray Level Co-occurrence Matrix (GLCM). Based on the results of tests carried out by the SOM algorithm with color and texture feature extraction parameters, it is able to assist in increasing the accuracy value. The results of testing the SOM model with color and texture feature extraction obtained a precision value of 93.33%, recall of 92.31% and accuracy of 92.78%.

A robust method is proposed in this paper to retrieve digital images using combination of color, texture and shape features. The proposed method consists of three stages. Shape detection is done based on TopHat transform to detect and crop object part of the image in the first stage. The second is included a texture feature extraction algorithm using color local binary patterns (CLBP) and local variance features together. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used as non-similarity ratio. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Shift invariant, Low noise sensitivity, rotation invariant are some of other advantages.

Improving color and shape repeatability of tongue images for diagnosis by using feedback gridlines

Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal weights. The color moment is used to find nine color features, Gray Level Co-occurrence Matrix (GLCM) to find four texture features, and Hu Moment to find seven shape features totaling 20 neurons and all of these features become the input layer in our MLP network. Three neurons in output layers become the automatic weight of each feature. These weights are used to combine each feature's role in obtaining the relevant image. Euclidean Distance is used to measure similarity. The average precision values obtained using automatic feature weights are 93.94% for the synthetic dataset, 91.19% for the Coil-100 dataset, and 54.31% for the Wang dataset. These results have an average difference of 5.06% with the target so automatic feature weighting works well. This value is obtained at a hidden layer size of 11 and a learning rate of 0.1. In addition, the use of automatic feature weighting gives more accurate results compared to manual feature weighting.

The application of image processing has been extended to agricultural industries in recent times. As the concept of image processing has vast application in agricultural industries, fruit grading has been focused in this research. There are number of approaches available for the classification and grading of mangoes, they suffer to achieve higher performance in classification. The most approaches uses color features and shape features only. To improve the performance of classification and fruit grading, an efficient GANFIS (Genetic Adaptive Neuro Fuzzy Inference System) is presented in this paper. The GANFIS approach reads the 2D mango images and extract various features like color, shape and texture features from the input image. Over the features extracted, the genetic algorithm has been applied to perform feature selection. With the extracted features, the method applies adaptive neuro fuzzy inference technique to perform classification and grading. The classification algorithm estimates multi feature class similarity MFCS measure towards each class of mangoes to perform classification where the grading is performed based on the same being estimated within the class. The proposed GANFIS approach has achieved sensitivity (98.05%), specificity (97.39%) and Accuracy (99.18%).

The crime of forgery of rupiah currency can be anticipated by examining the rupiah banknotes based on traits or features contained on the original paper money. Features that are not owned by the rupiah banknote counterfeit is an ultraviolet sign that are owned by the original paper money. Rupiah banknotes feature extraction consists of a combination of color and texture feature extraction. The proposed method is the HSV color histogram for color feature extraction and Segmented Fractal Texture Analysis (SFTA) for texture feature extraction. The combination of HSV and SFTA is expected to improve the performance of rupiah banknotes feature extraction. Moreover this paper will analyze feature redundancy in Two Threshold Decomposition Algorithm in SFTA Algorithm. Experimental results show the proposed method can reach 100% accuracy. Experiment results also show that redundant features can be removed without affecting the accuracy of of the system so that it can reduce the computational cost.

Image deraining is a crucial preprocessing task for enhancing the robustness of high-level vision systems under adverse weather conditions. However, most of the existing methods are limited to a single RGB color space, and it is difficult to effectively separate high-frequency rain streaks from low-frequency backgrounds, resulting in color distortion and detail loss in the restored image. Therefore, a rain removal network that combines dual-color space and frequency domain priors is proposed. Specifically, the devised network employs a dual-branch Transformer architecture to extract color and structural features from the RGB and YCbCr color spaces, respectively. Meanwhile, a Hybrid Attention Feedforward Block (HAFB) is constructed. HAFB achieves feature enhancement and regional focus through a progressive perception selection mechanism and a multi-scale feature extraction architecture, thereby effectively separating rain streaks from the background. Furthermore, a Wavelet-Gated Cross-Attention module is designed, including a Wavelet-Enhanced Attention Block (WEAB) and a Dual Cross-Attention module (DCA). This design enhances the complementary fusion of structural information and color features through frequency-domain guidance and bidirectional semantic interaction. Finally, experimental results on multiple datasets (i.e., Rain100L, Rain100H, Rain800, Rain12, and SPA-Data) demonstrate that the proposed method outperforms other approaches.

Color-based indexing is an important tool in image database retrieval. Compared with other features of the image, color features are less sensitive to noise and background complication. Based on the human visual system's perception of color information, this paper presents a dependent scalar quantization approach to extract the characteristic colors of an image as color features. The characteristic colors are suitably arranged in order to obtain a sequence of feature vectors. Using this sequence of feature vectors, a dynamic matching method is then employed to match the query image with database images for a nonstationary identification environment. The empirical results show that the characteristic colors are reliable color features for image database retrieval. In addition, the proposed matching method has acceptable accuracy of image retrieval compared with existing methods.

Flower classification is of great importance to the research fields of plants, food, and medicine. Due to more abundant information on three-dimensional (3D) flower models than two-dimensional 2D images, it makes the 3D models more suitable for flower classification tasks. In this study, a feature extraction and classification method were proposed based on the 3D models of Chinese roses. Firstly, the shape distribution method was used to extract the sharpness and contour features of 3D flower models, and the color features were obtained from the Red-Green-Blue (RGB) color space. Then, the RF-OOB method was employed to rank the extracted flower features. A shape descriptor based on the unique attributes of Chinese roses was constructed, χ2 distance was adopted to measure the similarity between different Chinese roses. Experimental results show that the proposed method was effective for the retrieval and classification tasks of Chinese roses, and the average classification accuracy was approximately 87%, which can meet the basic retrieval requirements of 3D flower models. The proposed method promotes the classification of Chinese roses from 2D space to 3D space, which broadens the research method of flower classification.

The term "Songket" comes from the Malay word "Sungkit", which means "to hook" or "to gouge". Every motifs names and variations was derived from plants and animals as source of inspiration to create many patterns of songket. Each of songket patterns have a philosophy in form of rhyme that refers to the nature of the sources of songket patterns and that philosophy reflects to the beliefs and values of Malay culture. In this research, we propose a system to facilitate an understanding of songket and the philosophy as a way to conserve Songket culture. We propose a system which is able to collect information in image songket motif variations based on feature extraction methods. On each image songket motif variations, we extracted philosophy of rhyme into impressions, and extracting color features of songket images using a histogram 3D-Color Vector quantization (3D-CVQ), shape feature extraction songket image using HU Moment invariants. Then, we created an image search based on impressions, and impressions search based on image. We use techniques of search based on color, shape and aggregation (combination of colors and shapes). The experiment using impression as query : 1) Result based on color, the average value of true 7.3, total score 41.9, 2) Result based on shape, the average value of true 3, total score 16.4, 3) Result based on aggregation, the average value of true 3, total score 17.4. While based using Image Query : 1) Result based on color, the average precision 95%, 2) Result based on shape, average precision 43.3%, 3) Based aggregation, the average precision 73.3%. From our experiments, it can be concluded that the best search system using query impression and query image is based on the color.Keyword : Image Search, Philosophy, impression, Songket, cultural computing, Feature Extraction, Analytical aggregation.

An image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the large database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and colour similarity. Retrieving images based on the contents which are colour, texture, and shape is called content-based image retrieval (CBIR). This paper discusses and describes about the colour features technique for image retrieval systems. Several colour features technique and algorithms produced by the previous researcher are used to calculate the similarity between extracted features. This paper also describes about the specific technique about the colour basis features and combined features (hybrid techniques) between colour and shape features.

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.

Web image retrieval technology is a hot research topic nowadays, many research institutions at home and abroad are engaged in the research of this topic, and has obtained certain research results. In this paper, at home and abroad about the Web image retrieval technology, on the basis of similarity matching algorithm for image retrieval technique and image analysis and verification. In the Web image retrieval, the color as an important visual features of image has been widely used. In this paper, the use of color features for image retrieval of three key problems: the representation of color, color features extraction and based on color similarity measure are discussed.