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Storyboard consisting of key-frames is a popular format of video summarization as it helps in efficient indexing, browsing and partial or complete retrieval of video. In this paper, we have presented a size constrained storyboard generation scheme. Given the shots i.e. the output of the video segmentation process, the method has two major steps: extraction of appropriate key-frame(s) from each shot and finally, selection of a specified number of key-frames from the set thus obtained. The set of selected key-frames should retain the variation in visual content originally possessed by the video. The number of key-frames or representative frames in a shot may vary depending on the variation in its visual content. Thus, automatic selection of suitable number of representative frames from a shot still remains a challenge. In this work, we propose a novel scheme for detecting the sub-shots, having consistent visual content, from a shot using Wald–Wolfowitz runs test. Then from each sub-shot a frame rendering the highest fidelity is extracted as key-frame. Finally, a spanning tree based novel method is proposed to select a subset of key-frames having specific cardinality. Chronological arrangement of such frames generates the size constrained storyboard. Experimental result and comparative study show that the scheme works satisfactorily for a wide variety of shots. Moreover, the proposed technique rectifies mis-detection error, if any, incurred in video segmentation process. Similarly, though not implemented, the proposed hypothesis test has ability to rectify the false-alarm in shot detection if it is applied on pair of adjacent shots.

A cosmetic product recognition system is proposed in this paper. For this recognition system, we have proposed a cosmetic product database that contains image samples of forty different cosmetic items. The purpose of this recognition system is to recognize Cosmetic products with there types, brands and retailers such that to analyze a customer experience what kind of products and brands they need. This system has various applications in such as brand recognition, product recognition and also the availability of the products to the vendors. The implementation of the proposed system is divided into three components: preprocessing, feature extraction and classification. During preprocessing we have scaled and transformed the color images into gray-scaled images to speed up the process. During feature extraction, several different feature representation schemes: transformed, structural and statistical texture analysis approaches have been employed and investigated by employing the global and local feature representation schemes. Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision Tree classifiers have been employed to perform the classification tasks. Apart from this, we have also performed some data analytic tasks for Brand Recognition as well as Retailer Recognition and for these experimentation, we have employed some datasets from the 'Kaggle' website and have obtained the performance due to the above-mentioned classifiers. Finally, the performance of the cosmetic product recognition system, Brand Recognition and Retailer Recognition have been aggregated for the customer decision process in the form of the state-of-the-art for the proposed system.

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