A Fast and Efficient Image Indexing and Search System based on Color and Texture Features

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.

Content based Image Retrieval (CBIR) allows automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. Colour and texture features are important properties in CBIR systems. In this paper, a combined feature descriptor for CBIR is proposed to enhance the retrieval performance for CBIR. This method is developed by exploiting the wavelets and colour histogram moments. First, Haar wavelet is used to decompose colour images into wavelet coefficients. Then image feature extraction and similarity matching are performed by means of histogram moments. Ten categories of colour images are used to test the proposed technique. Experiment results show significant improvement of retrieval efficiency as compared to that of histogram moments as well as 2D Discrete Wavelet Transform.

Research in content-based image retrieval today is a lively discipline, expanding in breadth. Discovering new and innovative processes of locating a desired image from an expanding collection of images has been a major area of interest for many professional fields. This has led to the increasing use of a technique called Content Based Image Retrieval which is used to retrieve images based on low-level features which are automatically driven such as color, shape and texture. CBIR draws many of its methods from the field of image processing and computer vision and can be regarded as subset of that field. Using this technique, the entire database may be searched to find the most closely matching image. In this paper, color and texture features of an image have been used to retrieve all the similar images from the image database. For this purpose, Block Color Histogram as color feature and Gray-level Co-occurrence Matrix (GLCM) and Gabor wavelets as texture features have been used. The proposed approach is very efficient since it uses the prominent and distinct features of an image useful for effective image retrieval using fuzzy heuristics according to human visual system.

According to the recent literature, it is pragmatic that image storage and retrieval through the World Wide Web (WWW) has made impressive progress. Practical searching for image still confronts us with present retrieval systems. A Content Based Image Retrieval (CBIR) system provides an efficient way of retrieving related images from image collections. In this paper we present a new technique to extract the images from the database to achieve better performance and efficiency. The novel method uses an approach, which combines texture information and modified Block Truncation Coding (BTC) method to extract the color features from the image. The whole image is divided into a fixed block of size 4 × 4, 8 × 8, 16 × 16, 32 × 32, 64 × 64, 128 × 128 and 256 × 256 non overlapped blocks. For each block, we calculated the threshold as a mean of the pixel values and the number of pixels which are lower than threshold value and upper than the threshold value are counted and mean of these lower and upper values have been taken and these two are considered as features. Now this process is repeated for the all blocks to extract the features of the image. The second step will compute the four texture features using the Gray-Level Co-occurrence Matrix (GLCM) as a statistical approach to extract energy, contrast, entropy and uniformity. We have used this modified BTC and texture features of the image to retrieve images. Finally, we have conducted experiments on a ground truth image database that has 1000 images of different categories. A different block sizes have been used to demonstrate the experimental results and compared with tabulation. From this we have observed that the performance is better and efficiency is low with smaller block sizes and vice versa with higher block sizes. So we have to choose the middle ranges of block sizes for better performance with better efficiency. As we have observed from the results that the performance of the retrieval system is improved because of combination of texture and color features. The proposed system's performance has been improved by an average precision of 2.41% and recall of 8.22% when compared to existing systems.

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.

Rapid development of multimedia technologies made Content Based Image Retrieval (CBIR) an energetic research area for the researchers of multimedia domain. Texture and color features have been the primal descriptors for images in the field of CBIR. This paper proposed a new CBIR system by combining the both color and texture features. Color Layout Descriptor (CLD) from MPEG-7 is used for the color feature extraction while, Mean, variance, skewness, Kurtosis, energy and entropy are used as texture descriptors. Experiments are performed on Coral Database. The results of the proposed method namely CLD-fos are compared with the four well reputed systems (i.e. SIMPLIcity, Histogram based, FIRM, and Variance Segment etc) from the industry. The results of the CLD-fos demonstrated high accuracy rate than the previous systems during the simulations. The proposed CLD-fos achieved significant performance in terms of accuracy.

In content based image retrieval (CBIR) process, every image has been represented in a compact set of local visual features i.e. color, texture, and/or shape of images. This set of local visual features is known as feature vector. In the CBIR process, feature vectors of images have been used to represent or to identify similar images in adequate way. As a result, feature vector construction has always been considered as an important issue since it must reflect proper image semantics using minimal amount of data. The proposed CBIR scheme is based on the combination of color and texture features. In this work initially, we have converted the given RGB image into HSV color image. Subsequently, we have considered H (hue), S (saturation), and V (intensity) components for extraction of visual image features. The texture features have been extracted from the V component of the image using 2D dual-tree complex wavelet transform (2D DT-CWT) where it analyzes the textural patterns in six different directions i.e. ±15\(^{\circ }\), ±45\(^{\circ }\), and ±75\(^{\circ }\). At the same time, we have computed the probability histograms of H and S components of the image respectively and subsequently those are divided into non-uniform bins based on cumulative probability for extraction of color based features. So, in this work both the color and texture features have been extracted simultaneously. Finally, the obtained features have been concatenated to attain the final feature vector and same is considered in image retrieval process. We have tested the novelty and performance of the proposed work in two Corel, two objects, and, a texture image datasets. The experimental results reveal the acceptable retrieval performances for different types of datasets.

Content Based Image Retrieval (CBIR) is a process in which for a given query image similar images will be retrieved based on the image content similarity. Image content refers to its visual features, which are mathematical representations of a digital image. The image retrieval task primarily depends on image feature extraction and similarity measurement between the feature vectors. The performance of CBIR process not only decided by the optimum features extracted from the image but also on the proper choice of the similarity/dissimilarity measures (distance metrics). As the image features broadly diversified in terms of color, texture and shape based, using the same distance metric may not work well for all of these features. In this paper, first we presented overview of geometric and statistical distance metrics used in CBIR along with the comparative analysis of these measures on color and texture features. Color features extracted by computing color histograms in HSV space and texture features by wavelet decompositions. Geometrical distances such as Manhattan, Chebyshev, Euclidean, statistical distance metrics such as Cosine Similarity, Chi-square, KullbackLeibler, Jeffrey and cumulative statistical distance metrics of Kolmogorov-Smirnov, Cramer von Mises and Earthmover's distances were analyzed for feature similarity. We gave certain conclusions on the performance of all these distance metrics in terms of Mean Average Precision (MAP) and Recall rates with color and texture features respectively.

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.

This research proposed the identification of chicken freshness level based on its color and texture features. Color Features used are the RGB (Red, Green, and Blue) and HSV (Hue, Saturation, Value) channel histogram value. texture features used are GLCM (Grey Level Co-Occurrence Matrix), Gabor kernel, and HOG (Histogram of Oriented Gradients). The freshness level of a chicken meat is categorized into three labels, fresh (0-4 hours after slaughtered), medium-fresh (4-6 hours after slaughtered), and not-fresh (more than 6 hours after slaughtered). The experiments will identify the freshness using several classification methods and different camera resolution and magnification. The highest classification accuracy using SVM (Support Vector Machines) achieves 58,33% with a smartphone camera, 98% with a webcam camera, and 79.1% with a 200 magnification digital microscope. From the experiment results, we can conclude that using webcam camera with normal resolution have better classification accuracy compared with a 200 magnification digital microscope or standard smartphone camera. It is also shown that SVM is superior compared with other methods tested in this experiments which are Decision Tree and Naive Bayes.

In this project, we proposed a Content Based Image Retrieval (CB IR) system which is used to retrieve a relevant image from an outsized database. Textile images showed the way for the development of CB IR. It establishes the efficient combination of color, shape and texture features. Herethe textile image is given as dataset. The images in database are loaded. The resultant image is given as input to feature extraction technique which is transformation of input image into a setof features such as color, texture and shape. The texture feature of an image is taken out by using Gray levelco-occurrence matrix (GLCM). The color feature of an image is obtained by HSIcolor space. The shape feature of an imageis extorted by sobel technique. These algorithms are used to calculate the similarity between extracted features. These features are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification techniques such as Support Vector M achine (SVM) are used to classify the features of a query image by splitting the group such as color, shape and texture.Finally, the relevant images are retrievedfrom a large database and hence the efficiency of an image is plotted.The software used is MATLAB7.10 (matrix laboratory) which is built software applications

The Content Based Image Retrieval (CBIR) is a technique that works on images and in response extracts relevant images. A novel hybrid two stage universal CBIR technique using both colour and texture features extraction is proposed in this paper. In the first stage for colour feature extraction, colour moments up to the fourth order are extracted and are used in deriving the respective histograms which forms the colour feature vector. In the second stage for the texture feature extraction the CCM (Colour Co-occurrence Matrix) technique employed takes into account the correlation between the RGB colour bands in all the eight directions while computing the texture features. In every stage the distance between the query image and the image in the database is calculated by using relative distance measure. The resultant distance between the query image and the image in the database is calculated by using a weighted distance classifier. Thus, a hybrid fusion method is achieved that has better performance than other colour-spatial based methods and promises to give more relevant output to the user.KeywordsCBIRlocal statistics histogramsSkewKurtosisCCM

Plants are an inevitable part of ecosystem. Tulsi leaves play a major role in plant species. There exists different varieties of tulsi leaves mainly Rama tulsi, Krishna tulsi, Vana tulsi, Kapoor tulsi etc. This paper proposes a system for identifying and classifying different types of tulsi from its leaf images by analyzing color and texture. Rama tulsi, Krishna tulsi and ordinary tulsi are the three varieties of tulsi leaves used for classification. Initially, a dataset is formed from the images of leaves. The feature values of these images are computed and it becomes the training data for the system. When a test image is scanned its color and texture features are extracted. Using K nearest neighborhood algorithm the extracted features are compared with the training data to display category of the leaf. Texture features are obtained using Gray Level Co-occurrence matrix (GLCM) whereas color features are acquired using color moments. Fusions of these techniques are used in this paper.

We implement an efficient image retrieval system via a content-based approach in the relational database system. In our system, as the preprocessing stages, we extract the color and texture features from the input image to get the retrieval keywords in the image database. First, our system classifies the images roughly by the color features using the ART2 neural network model. The classified information is stored in a relational database with the original image. To retrieve images similar to the query image, we extend the relational SQL statement coupled with the ART2 function in the WHERE condition clause. Next, to retrieve final results of images, we compare the query image with the candidate images which have the same category information by the texture features. The proposed system is implemented on Oracle DBMS. It shows satisfactory retrieval results in a test of the sample image database.

Plant disease is main reason of agricultural crops production losses. Leaf disease in plant occurs due to fungai, virus and bacterias. Image contains various important features which is used in classification. In this paper author initially detect disease then classify disease using extracted features. It takes five diseased leaves (Black rot, Black Measles, Leaf blight, Septoria leaf spot, Bacterial spot) and healthy leaf images then identify leaf is diseased or healthy then if leaf is diseased then classify type of disease. Color features mean, standard deviation, skewness and kurtosis computed then Region based shape feature calculated to identify the size of spots. Texture feature calculated using gray level co-occurrence matrix (GLCM) which identifies texture of image using distance and 45° angle variation in GLCM. Extracted features sent to trained feed forward neural network and classify diseased with color, shape and texture feature individually and combination of all features then observe that combination of color, shape and texture feature improve the performance of classification accuracy.