In this work many variations are introduced which are not used in the previous work in the same direction. We are focusing on color and texture information ofimage. First we are separating the image into R, G, B planes and then decomposing the image plane into 4 blocksand applying DCT transform over row mean vectors of each block of it to obtain the texture information of the image. The logic behind that DCT is a good approximation of principal component extraction, which helps to process and highlight the signal frequency features , , , , , . Same process is repeated with DCTwavelet transform over row mean vectors of each block of each plane. As Wavelets can be combined, using a "shift, multiply and sum" technique called convolution, with portions of an unknown signal to extract information from the unknown signal. They have advantages over traditional fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes , , , . This paper is organized as follows. Section II will introduce transforms applied to form the feature vectors. Section III gives the algorithmic flow of the system that explains how to extract the image contents and formation of the feature vector databases , , , . Section IV explains the experimental results with performance analysis of the system and Section V delineate the conclusion of the work done.
A typical content-based imageretrieval system can be described by the figure 1 here the visual contents of the images in the database are described and extracted by multi-dimensional feature vectors. This builds a feature database from the feature vectors. To retrieve images, users provide the retrieval system with query image. Feature vectors are then built. The distances between the feature vectors of the query imageandimagesof database are computed for similarity. In this paper we have used the features color, shape and texture and the relevant retrieval experiments show that the multi features retrieval brings better visual feeling than the single feature retrieval, which means better retrieval results. The results of our experiments are the compared with that of another approach. This Comparision clearly shows that our approach gives better performance over the approach used by Rao et al in their research.
A content based imageretrieval system using the colour and texture features of selected sub-blocksand global colour and shape features of the image is proposed. The colour features are extracted from the histograms of the quantized HSV color space, texture features from GLCM and shape features from EHD. A modified IRM algorithm is used for computing the minimum distance between the selected sub-blocksof the query imageand the candidate images in the database. Unlike the most sub-block based methods that involves all the sub-blocksof the query image to be compared with that of the candidate images, our system involves only selected sub-blocks for similarity measurement, thus reducing the number of comparisons and computational cost. Experimental results also show that the proposed method provides better retrieving result than some of the existing methods. Future work aims at the selection of sub-blocks based on their saliency in the image to improve the retrieval precision. Also the proposed method has to be tested on various databases to test the robustness.
Due to the factors like processing power limitations and channel capabilities images are often down sampled and transmitted at low bit rates resulting in a low resolution compressed image. High resolution images can be reconstructed from several blurred, noisy and down sampled low resolution imagesusing a computational process know as super resolution reconstruction. Super-resolution is the process of combining multiple aliased low-quality images to produce a high resolution, high-quality image. The problem of recovering a high resolution image progressively from a sequence of low resolution compressed images is considered. In this paper we propose a novel DCT based progressive image display algorithm by stressing on the encoding and decoding process. At the encoder we consider a set of low resolution images which are corrupted by additive white Gaussian noise and motion blur. The low resolution images are compressed using 8 by 8 blocksDCTand noise is filtered using our proposed novel zonal filter. Multiframe fusion is performed in order to obtain a single noise free image. At the decoder the image is reconstructed progressively by transmitting the coarser image first followed by the detail image. And finally a super resolution image is reconstructed by applying our proposed novel adaptive interpolation technique. We have performed both objective and subjective analysis of the reconstructed image, and the resultant image has better super resolution factor, and a higher ISNR and PSNR. A comparative study done with Iterative Back Projection (IBP) and Projection on to Convex Sets (POCS),Papoulis Grechberg, FFT based Super resolution Reconstruction shows that our method has out performed the previous contributions.
Abstract – Neural Networks offer the potential for providing a novel solution to the problem of data compression by its ability to generate an internal data representation. This network, which is an application of back propagation network, accepts a large amount ofimage data, compresses it for storage or transmission, and subsequently restores it when desired. A new approach for reducing training time by reconstructing representative vectors has also been proposed. Performance of the network has been evaluated using some standard real world images. Neural networks can be trained to represent certain sets of data. After decomposing an imageusing the Discrete Cosine Transform (DCT), a two stage neural network may be able to represent the DCT coefficients in less space than the coefficients themselves. After splitting the imageand the decomposition using several methods, neural networks were trained to represent the imageblocks. By saving the weights and bias of each neuron, by using the Inverse DCT (IDCT) coefficient mechanism an image segment can be approximately recreated. Compression can be achieved using neural networks. Current results have been promising except for the amount of time needed to train a neural network. One method of speeding up code execution is discussed. However, plenty of future research work is available in this area it is shown that the development architecture and training algorithm provide high compression ratio and low distortion while maintaining the ability to generalize and is very robust as well.
This paper presents a non-blind steganographic scheme that is based on the idea of data fusion. The method actually merges the normalized pixels of the secret image with the DCT coefficients of the cover image. Before this fusion phase, the cover image undergoes a compression step to reduce spatial redundancy using H.264 compression standard concepts. In addition, the algorithm applies an adjustment operation on the normalized cover pixels to guarantee that the message will be recovered with acceptable accuracy even when the value of embedding strength factor (α) is kept low. Experimental results showed that the proposed method can successfully hide an image into another one that is as large as itself while maintaining the fidelity of the stego-imageand providing almost perfect retrievalof the embedded secret message. When compared with other existing techniques, the results showed that the proposed algorithm achieved an outstanding invisibility performance as well as a remarkably high hiding capacity.
Vector quantization (VQ) has been a simple, efficient and attractive image compression scheme since the past three decades , . The basic VQ scheme partitions an image into small blocks (vectors) and each vector is assigned by an index of the codeword in the codebook for encoding . If the indices of the codewords are arranged in a specific form, they can effectively represent the image characteristics in terms ofimageblocks. For example, Yeh proposed a content-based imageretrieval algorithm for VQ images by analyzing the indices of codewords . Moreover, the correlations among these indices can be used to develop an efficient image segmentation scheme. Most of previous studies performed segmentation operations in the pixel level , . However, block-based image segmentation schemes can provide several advantages , . The block-based scheme is very suitable for the VQ-based image segmentation because VQ techniques usually divide an image into non-overlapping imageblocks. Block-based segmentation schemes segment an image with blocks rather than pixels by considering the relative information of the neighboring imageblocks. Large homogeneous regions can be detected by means of this technique. Moreover, the computational complexity can be saved by segmenting an image in the block level rather than in the pixel level.
Quellec et al  proposed a CBIR method for diagnosis in medical fields. In this, images are indexed in a generically, without extracting domain-specific features: a signature is built into each image from wavelet transform. These signatures characterize wavelet coefficient distribution in each decomposition subband. A distance measure compares two image signatures and retrieves most similar images from the database when a physician submits a query image. To retrieve relevant images from a medical database, signatures and distance measure should be related to medical image interpretation. Subsequently the system requires much freedom to tune it to any pathology with image modality being introduced. The scheme proposed using a custom decomposition scheme to adapt the wavelet basis with lifting scheme framework. Weights are introduced between subbands. All parameters are tuned by an optimization procedure, using database medical image grading to define performance measures. System assessment is through two medical image databases: one for diabetic retinopathy follow up and another for mammography screening, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% are achieved for the three databases, when the system returned five images.
In this way, three-component vector of HSV form One-dimensional vector, which quantize the whole color space for the 72 kinds of main colors. So we can handle 72bins of one-dimensional histogram. This quantification can be effective by reducing the computational time and complexity. It will be much of the deviation of the calculation of the similarity if we do not normalize, so we must normalize the components to the same range. The process of normalization is to make the components of feature vector equal importance. Color histogram is derived by first quantizing colors in the image into 72 bins in HSV color space, and counting the number ofimage pixels in each bin. One of the weaknesses of color histogram is that when the characteristics ofimages should not take over all the values, the statistical histogram will appear in a number of zero values. The emergence of these zero values would make similarity measure does not accurately reflect the color difference between imagesand statistical histogram method to quantify more sensitive parameters. Therefore, this paper represents the one-dimensional vector G by constructing a cumulative histogram of the color characteristics ofimage after using non-interval HSV quantization [12,13] for G.
image in a more robust way. Nan Wu and Min-Shiang Hwang  developed steganographic techniques for gray scale imagesand introduced schemes such as high hiding capacity schemes and high stego-image degradation imperceptibility schemes. Bo-Luen Lai and Long-Wen Chang  proposed a transform domain based adaptive data hiding method using Haar discrete wavelet transform. Most of the data was hidden in the edge region as it is insensitive to the human eye. Po-Yueh Chen and Hung-Ju Lin  developed a frequency domain based steganographic technique. The secret data is embedded in the high frequency coefficients/sub-bands of DWT and the low frequency sub-bands are not altered. Kang Leng Cheiew and Josef Pieprzyk  proposed a scheme to estimate the length of hidden message through histogram quotient in Binary image embedded by using Boundary pixels Steganography technique. Mahdi Ramezani and Shahrokh Ghaemmaghami  presented an Adoptive Steganography method with respect to image contrast thereby improving the embedding capacity of stego image contrast by selecting valid blocks for embedding based on average difference between the gray level values of the pixels in 2*2 blocksof non-overlapping spatially and their mean gray level. Saed Sarreshtedari and Shahrokh Ghaemmaghami  proposed a high capacity image steganography in Discrete Wavelet Transform (DWT) domain. HongmeiTang et al.,  suggested a scheme for image encryption and steganography by encrypting the message with a combination of gray value substitution operation and position permutation and then it is hidden in the cover image
There are some choices to generate feature vectors based on the nature of the fingerprint image. Logically, we need to examine what properties of background that differ from foreground. At least there are three properties of background and foreground that can be extracted to form features, namely intensity, homogenity and pattern. Background intensity is usually brighter than foreground intensity. It means that pixel values in the background area are higher than in the foreground area. Related to homogeneity, background area is more homogenous than foreground so that its variant is smaller than foreground variant. Patterns of background and foreground are more difficult to be measured numerically. Some measurements have been proposed to define patterns in fingerprint image, such as orientation or direction of ridges, the number of ridges and the thickness of ridges. These properties have been used extensively, but they are sensitive to noise and need long computation. To overcome the drawbacks we utilized feature generator that indirectly detects intensity, homogeneity and pattern as well. The generator that we chose is Haar wavelet decomposition. We used 2D Haar wavelet decomposition in two levels that decomposed original image into approximation and detail coefficients. Theoretically, all of these coefficients are resulted from linear transformation from the same data. So if we selected only one coefficient, it could reduce computation complexity without degrading the performance. In this method we chose the elements of approximation coefficient as vector feature. This feature consists only four elements. Sometimes the intensity of background pixels are close to furrows’ pixels intensity. It means that if the block size is too small, the furrows will be classified as background either. This problem might be solved by considering the size of the furrows. We observed that in 512 dpi fingerprint images, the furrows size are around 6 to 9 pixels. Therefore we chose blocksof size 8x8 pixels by considering that when this kind ofblocks reside in foreground area, they always contain part of furrows, so that those blocks will be classified as foreground.
image , but fail in terms of computational complexity; others proposed approaches reduce the blocking artifact but also reduce image sharpness. Chen et al  proposed a post processing filter of three modes to reduce blocking artifact between two adjacent blocksusing a fixed threshold values to classify the image into three frequency modes (smooth, intermediate, and non-smooth mode for low-frequency, mid-frequency, and high- frequency regions, respectively), to modify the current pixel values and its neighbors' across the block vertical and horizontal boundary. The modification is applied according to local image features after computing the difference between two pixels across the boundary. In an analogy to the prescribed DCT blocking artifacts; the application of fractional wavelet have some effects on image quality, because wavelet transform is applied on a fraction of an image each time, then all fractions are combined to form the complete constructed image which causes a little loss of correlation between every two adjacent fractions, resulting in some horizontal artifact at some pixels on the fraction boundary.
Nowadays, digital image compression has become a crucial factor of modern telecommunication systems. Image compression is the process of reducing total bits required to represent an image by reducing redundancies while preserving the image quality as much as possible. Various applications including internet, multimedia, satellite imaging, medical imaging uses image compression in order to store and transmit images in an efficient manner. Selection of compression technique is an application-specific process. In this paper, an improved compression technique based on Butterfly-Particle Swarm Optimization (BPSO) is proposed. BPSO is an intelligence-based iterative algorithm utilized for finding optimal solution from a set of possible values. The dominant factors of BPSO over other optimization techniques are higher convergence rate, searching ability and overall performance. The proposed technique divides the input image into 8 blocks. Discrete Cosine Transform 8 (DCT) is applied to each block to obtain the coefficients. Then, the threshold values are obtained from BPSO. Based on this threshold, values of the coefficients are modified. Finally, quantization followed by the Huffman encoding is used to encode the image. Experimental results show the effectiveness of the proposed method over the existing method.
Robustness: Robustness is a measure of the immunity of the watermark against attempts to remove or degrade it, internationally or unintentionally, by different types of digital signal processing attacks  . In this chapter, we will report on robustness results which we obtained for three major digital signal processing operations (attacks): Gaussian noise, image compression andimage cropping. The three attacks are a few, however, they are good representatives of the more general attacks. That is the Gaussian noise is a watermark degrading attack, JPEG compression is a watermark removal attack and cropping is a watermark dis- positioning geometrical attack. We measured the similarity between the original watermark and the watermark extracted from the attacked imageusing the correlation factor ρ given below in Eq. 6.
For feature extraction in CBIR there are mainly two approaches  feature extraction in spatial domain and feature extraction in transform domain. The feature extraction in spatial domain includes the CBIR techniques based on histograms , BTC [3,4,18], VQ [23,27,28]. The transform domain methods are widely used in image compression, as they give high energy compaction in transformed image [19,26]. So it is obvious to use images in transformed domain for feature extraction in CBIR . But taking transform ofimage is time consuming. Reducing the size of feature vector using pure image pixel data in spatial domain and getting the improvement in performance ofimageretrieval is shown in  & . Here those colour averaging based imageretrieval techniques are augmented using the even part ofimages obtained by adding the original image with the flip image. Many current CBIR systems use the Euclidean distance [3-5,10-16] on the extracted feature set as a similarity measure. The Direct Euclidian Distance between image P and query image Q can be given as equation 1, where Vpi and Vqi are the feature vectors ofimage P and Query image Q respectively with size ‘n’.
A solution to the problem is translated into a list of parameters known as chromosomes. These chromosomes are usually displayed as simple strings of data. In the first step, several characteristics are generated for the pioneer generation randomly and the relevant proportionality value is measured by the fitness function. The next step associates with the formation of the second generation of the society which is based on selection processes via genetic operators in accordance with the formerly set characteristics. A pair of parents is selected for every individual. Selections are devised so that to find the most appropriate component. In this way, even the weakest components enjoy their own chance of being selected and local solutions are bypassed. In the current study, Tournament method has been exploited.
Content based imageretrieval (CBIR) is the task of searching digital images from a large database based on the extraction of features, such as color, texture and shape of the image. Most of the research in CBIR has been carried out with complete queries which were present in the database. This paper investigates utility of CBIR techniques for retrievalof incomplete and distorted queries. Studies were made in two categories of the query: first is complete and second is incomplete. The query image is considered to be distorted or incomplete image if it has some missing information, some undesirable objects, blurring, noise due to disturbance at the time ofimage acquisition etc. Color (hue, saturation and value (HSV) color space model) and shape (moment invariants and Fourier descriptor) features are used to represent the image. The algorithm was tested on database consisting of 1875 images. The results show that retrieval accuracy of incomplete queries is highly increased by fusing color and shape features giving precision of 79.87%. MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.
Optimization methods are mainly concerned with large scale of applications like scientific and engineering fields. By the evolution of evolutionary algorithms, cost factor has reduced dramatically. In this research paper, the initial step is the best location is selected across different locations according to best features present in the environment and then the best optimized path is finding out in the same region avoiding all obstacles on the way like trees, rivers, oceans, forests etc. This approach focused on finding Cross country path with the Hybridization of two most popular swarm intelligence algorithm’s i.e. (firefly and Cuckoo). These two algorithms will help us finding the Cross country optimized path considering real world Impediments, which are often ignored in the process of making Robotic navigation system. As with the Capacity of firefly algorithm to detect Territories and attract towards interesting satellite image (with maximum information gain) out of Real time test data adding with the power of Cuckoo algorithm to use such Informational pixels in finding insight patterns of territories to find best area and locate best Cross country path which leads to the improvement in Robotic navigation for External vehicles. This would lead to better territory plotting, Impediment detection and escaping from unknown hurdles.
After understanding the concept of DT-CWT, next step was to implement it. In order to accomplish this we use C language. This code was synthesized using MATLAB. The gradient criterion based fusion in Dual Tree Complex Wavelet domain has been performed using various images from a standard image database or real time images. The robustness of the proposed fusion technique is verified successfully with some images such as: multi sensor image, multispectral remote sensing imagesand medical images such as CT, MR images, Surreal images .We can also fuse real time images.
The DWT transform: Wavelets are special functions which, in a form analogous to sines and cosines in Fourier analysis, are used as basal functions for representing signals . For 2D images, applying DWT corresponds to processing the image by 2D filters in each dimension. The filters divide the input image into four non- overlapping multi-resolution sub-bands LL1, LH1, HL1 and HH1. The sub-band LL1 represents the coarse-scale DWT coefficients while the sub-bands LH1, HL1 and HH1 represent the fine-scale of DWT coefficients. To obtain the next coarser scale ofwavelet coefficients, the sub-band LL1 is further processed until some final scale N is reached. When N is reached we will have 3N+1 sub- bands consisting of the multi-resolution sub-bands LLN and LHx, HLx and HHx where x ranges from 1 until N. Due to its excellent patio-frequency localization properties, the DWT is very suitable to identify the areas in the host image where a watermark can be embedded effectively. In particular, this property allows the exploitation of the masking effect of the human visual system such that if a DWT coefficient is modified, only the region corresponding to that coefficient will be modified.