Texture Based Image retrieval using Human interactive Genetic Algorithm

Content-based image retrieval has been keenly calculated in numerous fields. This provides more active management and retrieval of images than the keyword-based method. So the content based image retrieval has become one of the liveliest researches in the past few years. As earlier, we were using the text-based approach where it initiate very boring and hard task for solving the purpose of image retrieval. But the CBIR is the method where there are several methodologies are available and the task of image retrieval becomes well easier. In this, there are specific effective methods for CBIR are discussed and the relative study is made. However most of the proposed methods emphasize on finding the best representation for diverse image features. Here, the user-oriented mechanism for CBIR method based on an interactivegenetic algorithm (IGA) is proposed. Color attributes likethe mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition, the entropy based on the gray level co-occurrence matrix and the edge histograms of an image are too considered as the texture features

Segmentação não supervisionada de texturas baseada no algoritmo PPM

The image segmentation problem is present in various tasks such as remote sensing, object detection in robotics, industrial automation, content based image retrieval, security, and others related to medicine. When there is a set of pre-classified data, segmentation is called supervised. In the case of unsupervised segmentation, the classes are extracted directly from the data. Among the image properties, the texture is among those that provide the best results in the segmentation process. This work proposes a new unsupervised texture segmentation method that uses as the similarity measure between regions the bit rate obtained from compression using models, produced by the Prediction by Partial Matching (PPM) algorithm, extracted from them. To segment an image, it is split in rectangular adjacent regions and each of them is assigned to a different cluster. Then a greedy agglomerative clustering algorithm, in which the two closest clusters are grouped at every step, is applied until the number of remaining clusters is equal to the number of classes (supplied by the user). In order to improve the localization of the region boundaries, the image is then split in shorter regions, that are assigned to the cluster whose PPM model results in lower bit rate. To evaluate the proposed method, three image set were used: Trygve Randen, Timo Ojala and one created by the author of this work. By adjusting the method parameters for each image, the hit rate obtained was around 97% in most cases and 100% in several of them. The proposed method, whose main drawback is the complexity order, is robust to regions with different geometric shapes, grouping correctly even those that are disconnected.Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorO problema da segmentação de imagens está presente em diversas tarefas como sensoriamento remoto, detecção de objetos em robótica, automação industrial, recuperação de imagens por conteúdo, segurança, e outras relacionadas à medicina. Quando há um conjunto de padrões pré-classificados, a segmentação é denominada supervisionada. No caso da segmentação não supervisionada, as classes são extraídas diretamente dos padrões. Dentre as propriedades de uma imagem, a textura está entre as que proporcionam os melhores resultados no processo de segmentação. Este trabalho propõe um novo método de segmentação não supervisionada de texturas que utiliza como medida de similaridade entre regiões as taxas de bits resultantes da compressão utilizando modelos produzidos pelo algoritmo Prediction by Partial Matching (PPM) extraídos das mesmas. Para segmentar uma imagem, a mesma é dividida em regiões retangulares adjacentes e cada uma delas é atribuída a um grupo distinto. Um algoritmo aglomerativo guloso, que une os dois grupos mais próximos em cada iteração, é aplicado até que o número de grupos seja igual ao número de classes (fornecido pelo usuário). Na etapa seguinte, cujo objetivo é refinar a localização das fronteiras, a imagem é dividida em regiões ainda menores, as quais são atribuídas ao agrupamento cujo modelo PPM resulta na taxa de bits mais baixa. Para avaliar o método proposto, foram utilizados três bancos de imagens: o de Trygve Randen, o de Timo Ojala e um criado pelo autor deste trabalho. Ajustando-se os parâmetros do método para cada imagem, a taxa de acerto obtida foi em torno de 97% na maioria dos casos e 100% em vários deles. O método proposto, cuja principal desvantagem é a ordem de complexidade, se mostrou robusto a regiões de diferentes formas geométricas, agrupando corretamente até mesmo as desconexas

CONTENT-BASED IMAGE RETRIEVAL USING ENHANCED HYBRID METHODS WITH COLOR AND TEXTURE FEATURES

Content-based image retrieval (CBIR) automatically retrieves similar images to the query image by using the visual contents (features) of the image like color, texture and shape. Effective CBIR is based on efficient feature extraction for indexing and on effective query image matching with the indexed images for retrieval. However the main issue in CBIR is that how to extract the features efficiently because the efficient features describe well the image and they are used efficiently in matching of the images to get robust retrieval. This issue is the main inspiration for this thesis to develop a hybrid CBIR with high performance in the spatial and frequency domains. We propose various approaches, in which different techniques are fused to extract the statistical color and texture features efficiently in both domains. In spatial domain, the statistical color histogram features are computed using the pixel distribution of the Laplacian filtered sharpened images based on the different quantization schemes. However color histogram does not provide the spatial information. The solution is by using the histogram refinement method in which the statistical features of the regions in histogram bins of the filtered image are extracted but it leads to high computational cost, which is reduced by dividing the image into the sub-blocks of different sizes, to extract the color and texture features. To improve further the performance, color and texture features are combined using sub-blocks due to the less computational cos

Digital photo album management techniques: from one dimension to multi-dimension.

Lu Yang.Thesis submitted in: November 2004.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 96-103).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Our Contributions --- p.3Chapter 1.3 --- Thesis Outline --- p.5Chapter 2 --- Background Study --- p.7Chapter 2.1 --- MPEG-7 Introduction --- p.8Chapter 2.2 --- Image Analysis in CBIR Systems --- p.11Chapter 2.2.1 --- Color Information --- p.13Chapter 2.2.2 --- Color Layout --- p.19Chapter 2.2.3 --- Texture Information --- p.20Chapter 2.2.4 --- Shape Information --- p.24Chapter 2.2.5 --- CBIR Systems --- p.26Chapter 2.3 --- Image Processing in JPEG Frequency Domain --- p.30Chapter 2.4 --- Photo Album Clustering --- p.33Chapter 3 --- Feature Extraction and Similarity Analysis --- p.38Chapter 3.1 --- Feature Set in Frequency Domain --- p.38Chapter 3.1.1 --- JPEG Frequency Data --- p.39Chapter 3.1.2 --- Our Feature Set --- p.42Chapter 3.2 --- Digital Photo Similarity Analysis --- p.43Chapter 3.2.1 --- Energy Histogram --- p.43Chapter 3.2.2 --- Photo Distance --- p.45Chapter 4 --- 1-Dimensional Photo Album Management Techniques --- p.49Chapter 4.1 --- Photo Album Sorting --- p.50Chapter 4.2 --- Photo Album Clustering --- p.52Chapter 4.3 --- Photo Album Compression --- p.56Chapter 4.3.1 --- Variable IBP frames --- p.56Chapter 4.3.2 --- Adaptive Search Window --- p.57Chapter 4.3.3 --- Compression Flow --- p.59Chapter 4.4 --- Experiments and Performance Evaluations --- p.60Chapter 5 --- High Dimensional Photo Clustering --- p.67Chapter 5.1 --- Traditional Clustering Techniques --- p.67Chapter 5.1.1 --- Hierarchical Clustering --- p.68Chapter 5.1.2 --- Traditional K-means --- p.71Chapter 5.2 --- Multidimensional Scaling --- p.74Chapter 5.2.1 --- Introduction --- p.75Chapter 5.2.2 --- Classical Scaling --- p.77Chapter 5.3 --- Our Interactive MDS-based Clustering --- p.80Chapter 5.3.1 --- Principal Coordinates from MDS --- p.81Chapter 5.3.2 --- Clustering Scheme --- p.82Chapter 5.3.3 --- Layout Scheme --- p.84Chapter 5.4 --- Experiments and Results --- p.87Chapter 6 --- Conclusions --- p.94Bibliography --- p.9

CONTENT-BASED IMAGE RETRIEVAL USING ENHANCED HYBRID METHODS WITH COLOR AND TEXTURE FEATURES

Content-based image retrieval (CBIR) automatically retrieves similar images to the query image by using the visual contents (features) of the image like color, texture and shape. Effective CBIR is based on efficient feature extraction for indexing and on effective query image matching with the indexed images for retrieval. However the main issue in CBIR is that how to extract the features efficiently because the efficient features describe well the image and they are used efficiently in matching of the images to get robust retrieval. This issue is the main inspiration for this thesis to develop a hybrid CBIR with high performance in the spatial and frequency domains. We propose various approaches, in which different techniques are fused to extract the statistical color and texture features efficiently in both domains. In spatial domain, the statistical color histogram features are computed using the pixel distribution of the Laplacian filtered sharpened images based on the different quantization schemes. However color histogram does not provide the spatial information. The solution is by using the histogram refinement method in which the statistical features of the regions in histogram bins of the filtered image are extracted but it leads to high computational cost, which is reduced by dividing the image into the sub-blocks of different sizes, to extract the color and texture features. To improve further the performance, color and texture features are combined using sub-blocks due to the less computational cos