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

Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain

AbstractThe effective content-based image retrieval (CBIR) needs efficient extraction of low level features like color, texture and shapes for indexing and fast query image matching with indexed images for the retrieval of similar images. Features are extracted from images in pixel and compressed domains. However, now most of the existing images are in compressed formats like JPEG using DCT (discrete cosine transformation). In this paper we study the issues of efficient extraction of features and the effective matching of images in the compressed domain. In our method the quantized histogram statistical texture features are extracted from the DCT blocks of the image using the significant energy of the DC and the first three AC coefficients of the blocks. For the effective matching of the image with images, various distance metrics are used to measure similarities using texture features. The analysis of the effective CBIR is performed on the basis of various distance metrics in different number of quantization bins. The proposed method is tested by using Corel image database and the experimental results show that our method has robust image retrieval for various distance metrics with different histogram quantization in a compressed domain

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

The Impact of Dispositional Optimism and Self-determination on Wellbeing of Job Seeker Young Adults

The present study aimed to find out that 1) dispositional optimism and self-determination are positively related to well-being in job seekers young adults, and 2) to find out the predicting role of dispositional optimism and self-determination in determining the well-being of job seekers. The study was based on a correlational research design. A purposive sample of 192 job seekers young adults aged 19 to 27 years (M =22, SD=1.25) was taken as a sample. The sample consisted of 91 men and 101 women from four different universities in Lahore. Urdu versions of the Life orientation test-revised (Scheier et al., 1985), Self-determination scale (Deci & Ryan, 2000), Mental health continuum short-form (Keyes & Ryff, 1998) and self-constructed demographic information sheet were used for assessment. The results showed that dispositional optimism, self-determination, and well-being are positively related to young job seekers. Further, dispositional optimism and self-determination were found as positive predictors of well-being in job-seeking young adults. Further, the results also indicated that men have higher social well-being as compared to women. The limitations and suggestions are also discussed

Virulence profiling of Shigella flexneri and emergence of serotype 2b as a highly virulent shigellosis causing strain in Pakistan

Bacillary diarrhea caused by Shigella flexneri is mediated by various virulence factors which make it the leading agent of diarrhea in developing countries. Previously, a high prevalence of S. flexneri, associated with diarrhea has been reported in Pakistan but no data is available on their virulence profile. The present study reporting for the first time analysis of various virulence factors among S. flexneri serotypes isolated from clinical (diarrheal stool) and non-clinical (retail raw foods and drinking water) sources. A total of 199 S. flexneri (clinical: 155, raw foods: 22, water: 22) belonging to various serotypes were subjected to virulence genes detection and virulence profiling. The most frequent virulence gene was found to be ipaH (100%), followed by sat (98%), ial (71.3%), set1B (65.8%) and set1A (38.7%). A high level of virulence was detected in serotype 2b as compared to other serotypes as 32.3% of all serotype 2b have the entire set of five virulence genes including ipaH (100%), ial (100%), sat (37.7%), set1A (89.3%), and set1B (100%). Seven different virulence gene profiles (V1 - V7) were detected and the most frequently observed to be V1 (ipaH+, ial+, sat+, set1A+, set1B+) followed by V3 (ipaH+, ial+, sat+, set1B+). The predominant virulence gene pattern in serotype 2b isolated from clinical and non-clinical samples were V1 and V3. Furthermore, about 32% strains belongs to serotype 2b contain the complete set of five virulence genes isolated from patients with high disease severity. In conclusion, the current finding revealed for the first times that serotype 2b was the most virulent strains in both clinical and non-clinical samples in Pakistan. In addition, the virulence of serotype 2b was well correlated with high disease severity

Perspective Chapter: Genomics, Proteomics, and System Biology of Insecticides Resistance in Insects

Insecticide resistance is an inherited change in pest population exposure to a specific insecticide or group of insecticides. Overuse, misuse, and high interbreeding rates have led to insecticide resistance. Genomic technologies reveal mechanisms of resistance, including decreased target-site sensitivity and increased detoxification. Genomic projects have cloned and identified targeted genes in Drosophila melanogaster and studied resistance-associated mutations in various pest insects. Advancements in genome sequencing and annotation techniques have explored complex multigene enzyme systems, such as glutathione-S-transferases, esterases, and cytochrome P450, which facilitate insecticide resistance. Identifying specific genes involved in resistance and targeted genes is essential for developing new insecticides and strategies to control pests. Insects with resistance metabolize insecticidal compounds faster due to increased catalytic rate and gene amplification. So, system biology plays a very important role in the insect resistance against insecticides and different chemicals such as DDT and permethrin. From system biology, not only the identification of genes was done, but also the protein-protein interactions were found out, which were responsible in the insect resistance