WILD ANIMAL DETECTION USING MULTI-CLUSTER FEATURE SELECTION - Keerthana, S; shyla, E. Mary (International Journal of Computer Sciences and Engineering, 2025)
Wild animal detection helps wildlife researchers to analyze and study wild animal habitat and behavior. Discriminative Feature-oriented Dictionary Learning (DFDL) was utilized for learning discriminative features of positive images that have animals present in positive class, in addition of negative images that do not have animals present in that class. But, this approach has low performance for detection of visual wild animals. Hence, in this paper, Multi-Cluster Feature Selection (MCFS) is proposed for unsupervised feature selection and wild animal detection. Those features are chosen, which the multi-cluster structure of the data is well preserved. Based on spectral analysis approaches, the proposed method suggests a principled manner for calculating the correlations among various features without label information. Thus, the proposed technique handles the data with multiple cluster structure. The experimental results show that the proposed approach provides the better results