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Traditional Uses Of Plants By The Tribal Communities Of V. CONCLUSIONS The popular use of herbal remedies among the tribal people in Parvathipuram hill range of Visakhapatnam district reflects the revival of interest in traditional medicine. The scientific validation of these remedies may help in discovering new drugs from the plant species. The information on therapeutic uses of plants may provide a great potential for discovering of new drugs and promoting awareness among the people to use them as remedy in health care system. VI. ACKNOWLEDGEMENT The authors are grateful to the Forest officials of Andhra Pradesh for permitting us to make field visits in Salugu Panchayati hill range of Visakhapatnam District. Our thanks also to tribal people in the study area. REFERENCE [1]. Rao, V.L.N., B. R. Busi, B. Dharma Rao, Ch. Seshagiri Rao & M. Vemkaiah. Ethnomedicinal practices among Khonds of Visakhapatnam district, Andhra Pradesh. Indian Journal of Traditional Knowledge Vol. 5 (2). 2006, 217--219. [2]. Pal, D.C. & D.K. Banerjee. Some less known plant foods among the tribals of Andhra Pradesh and Orissa states. 1971, Bull. Bot. Surv. India 13: 221-223. [3]. Nisteswar, K. & K. A. Kumar. Utilization values of medical-lore of Rampa Agency (Andhra Pradesh) in primary health care, 1980. Sachitra Ayurved 33: 210-212. [4]. Nisteswar, K. & K.A. Kumar. Equity in folklore medicine of Addateegala Agency tract of East Godavari district of Andhra Pradesh. Vaghabhata , 1983. 1: 43-44. [5]. Ramarao, N. & A.N. Henry. The Ethnobotany of Eastern Ghats in Andhra Pradesh, India, 1996. Botanical Survey of India, Kolkata. [6]. Jain SK. A manual of Ethnobotany. 1987, Scientific publishers, Jodhpur, India. [7]. Hemdri K, CRR Sarma and SS Rao. Medicinal plant wealth of Andhra Pradesh, Part-I. Ancient Science of Life. 1987, 6:167-186. [8]. Hemdri K CRR Sarma and SS Rao. Medicinal plant wealth of Andhra Pradesh, Part-II. Ancient Science of Life. 1987, 7(5)5-64. [9]. Jeevn Ram A, RV Reddy, M Adharvana Chari and RR Venkata Raju. Rare and Little known medicinal plants from Nallamalais of the Eastern Ghats, India. Journal of Plant Science. 2007. 2(1) 113-117. [10]. Madhusudan Rao A, A floristic studies on the Flora of Kadapa District, A.P. India, Ph.D., Thesis, 1989, Sri Venkateswara University, Tirupathi. [12]. Jain S.K. Dictionary of Indian folk medicine and Ethnobotany. Deep publications, 1991, New Delhi. [13]. Jain SK and RR Rao. Hand Book of Field and Herbarium Methods. (Today and Tomorrow Printers and Publishers), 1997, BSI, Calcutta. [14]. Gamble JS and Fischer CEC. Flora of the Presidency of Madras, 1-3 (Adlard & Son, London), 1915-1936. [15]. Chandra Babu N, MTarakeswar Naidu and M Venkaiah. Ethnobotanical plants of Kotia hills of Vizianagaram district, Andhra Pradesh, India. Journal of Phytology. 2010, 2(6): 76-82. www.<strong>ijcer</strong>online.com ||May ||2013|| Page 103
International Journal of Computational Engineering Research||Vol, 03||Issue, 5|| Content Based Video Retrieval Using Cluster Overlapping Deepak C R 1 , Sreehari S 2 , Gokul M 3 , Anuvind B 4 1, 3(Computer Science, Amrita School of Engineering/ Amrita vishwa vidyapeetham, Coimbatore, India) 2(Electrical and Electronics, Amrita School of Engineering/ Amrita vishwa vidyapeetham, Coimbatore, India) 4(Electronics and Communication, Amrita School of Engineering/ Amrita vishwa vidyapeetham, Coimbatore, India) ABSTRACT: To retrieve videos from database efficient video indexing and retrieval mechanisms are required. In this paper, we propose an efficient algorithm to retrieve videos from the database when a video clip is given as a query. To efficiently match query video clip with the videos in the database various spatio-temporal features are used. Clustering algorithms are applied to extracted features for fast retrieval. Cluster overlapping method is used to retrieve relevant videos. Relevant videos are ranked based on the similarity measurement and frequency of query shots in the retrieved videos. Experimental result proves that proposed method has high precision and recall compared to conventional algorithms. Keywords: Content based video retrieval, key frame, video indexing, shot clustering I. INTRODUCTION There are various content based video retrieval and indexing methods which uses spatio-temporal features. For video indexing, Jianping Fan et al. [1] proposed hierarchical video shot classification method. In hierarchical video shot classification method video is structured in to hierarchical tree using semantics of the video. H. Farouk et al. [2] proposed video indexing and retrieval algorithm using wavelet coefficients that are extracted from video frames. Ja-Hwung Su et al. [3] proposed video indexing and retrieval mechanism using pattern trees constructed from the video shot patterns in a video. They have used fast pattern index tree and advanced pattern index tree for indexing and ranking of the videos. Stacie Hibino and Eke A. Rundensteiner analyzed videos in terms of temporal relationship between events [4]. First step in video indexing and retrieval is temporal video segmentation. Various methods are proposed for temporal video segmentation [5], [6], [7], [8], which are efficient in dividing video into shots. II. PROPOSED METHOD In the proposed system we have used various spatio-temporal features of the video and clustering methods to efficiently retrieve videos from the database when a video clip is given as a query. Intersection of various clusters is found in order to find the relevant videos from the database. The shots which are present in the intersection of relevant clusters are ranked based on the similarity between query shots and their frequency of appearance in the videos. Detailed architectural diagram is given in figure 1. 2.1. Spatio-temporal features Database videos are divided into shots using combined color, edge and motion features [9]. Using these features, adjacent frames in video are compared in order to find the shot boundary. One key frame is selected from the each shot using mutual information and image entropy [10]. Key frames selected from videos are given in figure 2. For getting color information from keyframes, RGB color histogram is used. Before the extraction of color histogram each frame in a video is quantized in to 86 intensity levels. Texture information from a frame is calculated using tamura features [11]. Features that are extracted for texture information are coarseness, directionality, regularity and degree of contrast. Canny edge detector is used to extract edge information [12]. For extracting edge features each frame is divided into 16 sub-blocks and edge direction and its count from each block is calculated. Edge direction information is obtained by convolving edge image with various filters. Optical flow algorithm is used for getting motion vectors from the keyframe. Another temporal feature extracted from video shots is camera motion. Camera motion in a video is classified into irregular camera motion, still camera, smooth camera, little camera motion and no camera motion [13]. In the same manner motion vectors in the key frame is classified into four type no motion, small motion, medium motion and large motion. After finding motion vectors using Lucas Kanade’s algorithm frame is divided into 16 sub-blocks. For each block based on the motion vectors we determine motion of that block as one among the four types of motion vectors- no motion, large motion, small motion, and medium motion. www.<strong>ijcer</strong>online.com ||May ||2013|| Page 104
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