K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

A Fast Object Recognition Using Edge Texture Analysis for Image Retrieval

A Robust Object Recognition for Content Based Image Retrieval (CBIR) based on Discriminative Robust Local Binary Pattern (DRLBP) and Local Ternary Pattern (LTP) analysis. The Robust Object Recognition using edge and texture feature extraction. The extension of Local Binary Pattern (LBP) is called DRLBP. The category recognition system will be developed for application to image retrieval. The category recognition is to classify an object into one of several predefined categories. LBP is defined as an ordered set of binary comparisons of pixel intensities between the center pixel and its eight surrounding pixels .DRLBP features identifying the contrast information of image patterns. The proposed features preserve the contrast information of image patterns. The DRLBP discriminates an object like the object surface texture and the object shape formed by its boundary