Content‐based image retrieval (CBIR) systems require users to query images by their low‐
level visual content; this not only makes it hard for users to formulate queries, but also can …

With the success of deep learning in the field of computer vision, object recognition has
made important breakthroughs, and its recognition accuracy has been drastically improved …

State-of-the-art object detectors typically use shape information as a low level feature
representation to capture the local structure of an object. This paper shows that early fusion …

In this paper, we present a feature description method called semantic descriptor with
objectness (SDO) for scene recognition. Most existing scene representation methods exploit …

Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary
research domain. Most of the research in the analysis of paintings involve medium to small …

In this article we investigate the problem of human action recognition in static images. By
action recognition we intend a class of problems which includes both action classification …

mCENTRIST, a new multichannel feature generation mechanism for recognizing scene
categories, is proposed in this paper. mCENTRIST explicitly captures the image properties …

Color description is a challenging task because of large variations in RGB values which
occur due to scene accidental events, such as shadows, shading, specularities, illuminant …

The main objective of this research paper is to design a system which would generate
multimodal, nonparametric Bayesian model, and multilayered probability latent semantic …

Person description is a challenging problem in computer vision. We investigated two major
aspects of person description: 1) gender and 2) action recognition in still images. Most state …