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

An Image Indexing and Region based on Color and Texture

From the previous decade, the enormous rise of the internet has tremendously maximized the amount image databases obtainable. This image gathering such as art works, satellite and medicine is fascinating ever more customers in numerous application domains. The work on image retrieval primarily focuses on efficient and effective relevant images from huge and varied image gatherings which is further becoming more fascinating and exciting. In this paper, the author suggested an effective approach for approximating large-scale retrieval of images through indexing. This approach primarily depends on the visual content of the image segment where the segments are obtained through fuzzy segmentation and are demonstrated through high-frequency sub-band wavelets. Furthermore, owing to the complexity in monitoring large scale information and exponential growth of the processing time, approximate nearest neighbor algorithm is employed to enhance the retrieval speed. Thus, a locality-sensitive hashing using (K-NN Algorithm) is adopted for region-aided indexing technique. Particularly, as the performance of K-NN Approach hinges essentially on the hash function segregating the space, a novel function was uncovered motivated using E8 lattice which could efficiently be amalgamated with multiple probes K-NN Approach and query-adaptive K- NN Approach. To validate the adopted hypothetical selections and to enlighten the efficiency of the suggested approach, a group of experimental results associated to the region-based image retrieval is carried out on the COREL data samples

An information-driven framework for image mining

[Abstract]: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how low-level, pixel representation contained in a raw image or image sequence can be processed to identify high-level spatial objects and relationships. To meet this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful patterns/knowledge from each level

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio