Content Based Video Retrieval using SURF Descriptor

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.

Studies On Sediment Transport In The Surf Zone Along Certain Beaches Of Kerala

Sediment transport in the nearshore areas is an important process in deciding the coastline stability. The design and effective maintenance of navigable waterways, harbours and marine structures depend on the stability of the sediment substrate and the nature of sedimentation in the nearshore zone. The nearshore zone is a complex environment and the exact relationships existing between water motions and the resulting sediment transports are not well understood. During the rough weather season, when the sediment movement is considerable, processes occurring in the nearshore area are much less understood. Moreover, there is a general lack of field measurements, especially during the time of severe storm conditions. The increasing pressures and the concern on the preservation of the valuable coastal environment have led to the development of shore protection programmes. Conservation not only demands knowledge of what needs to be done, but also requires the basic processes to be fully understood. Considering the fragile nature of barrier beaches and intense occupancy of these areas by man, these coastal features have long been a subject of study by coastal oceanographers, geomorphologists and engineers. The present study is an attempt to understand the sediment movement in relation to beach dynamics, especially in the surf zone, along some part of Kerala coast and the response of the beaches to various forcing functions over different seasons

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

Performance Study of an Improved Legendre Moment Descriptor as Region-Based Shape Descriptor

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision- recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications

Development of Shape Descriptors Based on Legendre Polynomials and Content-Based Retrieval of Scoliosis Images

The wealth of information available freely on the web and medical image databases poses a major problem for the end users: how to find the information needed? Content –Based Image Retrieval is the obvious solution.A standard called MPEG-7 was evolved to address the interoperability issues of content-based search.The work presented in this thesis mainly concentrates on developing new shape descriptors and a framework for content – based retrieval of scoliosis images.New region-based and contour based shape descriptor is developed based on orthogonal Legendre polymomials.A novel system for indexing and retrieval of digital spine radiographs with scoliosis is presented.

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching.

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

Detergent compatible alkaline lipase produced by marine Bacillus smithii BTMS 11

Bacillus smithii BTMS 11, isolated from marine sediment, produced alkaline and thermostable lipase. The enzyme was purified to homogeneity by ammonium sulfate precipitation and ion exchange chromatography which resulted in 0.51 % final yield and a 4.33 fold of purification. The purified enzyme was found to have a specific activity of 360 IU/mg protein. SDS-PAGE analyses, under non-reducing and reducing conditions, yielded a single band of 45 kDa indicating the single polypeptide nature of the enzyme and zymogram analysis using methylumbelliferyl butyrate as substrate confirmed the lipolytic activity of the protein band. The enzyme was found to have 50 C and pH 8.0 as optimum conditions for maximal activity. However, the enzyme was active over wide range of temperatures (30–80 C) and pH (7.0–10.0). Effect of a number of metal salts, solvents, surfactants, and other typical enzyme inhibitors on lipase activity was studied to determine the novel characteristics of the enzyme. More than 90 % of the enzyme activity was observed even after 3 h of incubation in the presence of commercial detergents Surf, Sunlight, Ariel, Henko, Tide and Ujala indicating the detergent compatibility of B. smithii lipase. The enzyme was also found to be efficient in stain removal from cotton cloths. Further it was observed that the enzyme could catalyse ester synthesis between fatty acids of varying carbon chain lengths and methanol with high preference for medium to long chain fatty acids showing 70 % of esterification. Results of the study indicated scope for application of this marine bacterial lipase in various industries