Saliency and Burstiness for Feature Selection in CBIR

The paper addresses the problem of visual feature selection in content-based image retrieval (CBIR). We propose to study two strategies: the first one is using visual saliency, that selects the most salient features of the image and the second one exploits burstiness, that detects and processes the repeated visual elements in the image. To detect and describe the visual features in images, we rely on a deep local features approach based on convolutional neural network. The two strategies are evaluated for image retrieval on different datasets, according to two criteria: quality of retrieval and volume of manipulated features.