Abstract
Smart Content Based Image Retrieval (CBIR) helps to simultaneously localize and recognize all object(s) present in a scene, for image retrieval task. The major drawbacks in such kind of system are: (a) overhead for addition of new class is high - addition of new class requires manual annotation of large number of samples and retraining of an entire object model; and (b) use of handcrafted features for recognition and localization task, which limits its performance. In this era of data proliferation where it is easy to discover new object categories and hard to label all of them i.e. less amount of labeled samples for training which raises the above mentioned drawbacks. In this work, we propose an approach which cuts down the overhead of labelling the data and re-training on an entire module to learn new classes. The major components in proposed framework are: (a) selection of an appropriate pre-trained deep model for learning a new class; and (b) learning new class by utilizing selected deep model with less supervision (i.e. with the least amount of labeled data) using a concept of triplet learning. To show the effectiveness of the proposed technique of new class learning, we have performed an evaluation on CIFAR-10, PASCAL VOC2007 and Imagenet datasets.
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Pahariya, G., Ravindran, B., Das, S. (2018). Dynamic Class Learning Approach for Smart CBIR. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_29
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