Sparse Code based Indexing for Image Retrieval

Recent technological developments have led to the rapid creation of large datasets of images at a low cost. Thus, it is necessary to develop methods capable of retrieving images from gigantic datasets quickly and reliably. Given the high computational cost of this task, it is necessary to create a compact representation of each element in the set of images to be searched. Sparse representation makes it possible to perform searches. Sparse signal representation is a powerful tool for capturing, representing and compressing signals in high dimensions. This collection is usually called a “dictionary”, and its elements are called “words” or “Atoms”. The idea of sparse representation represents images through a linear combination of few atoms in the dictionary. This research work is proposed to build an inverted index using sparse coding to ensure the relevant image retrieval. This work is devoted to studying and developing methods for image retrieval using dictionary learning. The proposed research work has developed a framework for dictionary learning and coefficient learning simultaneously. Here, KSVD based approach is used for dictionary learning and three optimization schemes viz. OR, LARS, Elastic-Net are used to optimize the sparse codes. From various aspects, the proposed framework shows consistent performance over existing approaches on two publically available & widely acceptable datasets, viz Coil and Caltech-256.